{"task":"3D Point Cloud Classification","dataset":"ModelNet40","metric_names":["Overall Accuracy","Mean Accuracy","Number of params","FLOPs","Mean class accuracy"],"rows":[{"id":83495,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointGST","metrics":{"Overall Accuracy":"95.3"},"paper_url":"https://arxiv.org/abs/2410.08114v1","paper_title":"Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning","paper_date":"2024-10-10","code_links":[{"title":"jerryfeng2003/pointgst","url":"https://github.com/jerryfeng2003/pointgst"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83496,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Mamba3D + Point-MAE","metrics":{"FLOPs":"3.9G","Number of params":"16.9M","Overall Accuracy":"95.1"},"paper_url":"https://arxiv.org/abs/2404.14966v2","paper_title":"Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model","paper_date":"2024-04-23","code_links":[{"title":"xhanxu/Mamba3D","url":"https://github.com/xhanxu/Mamba3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83497,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ReCon++","metrics":{"Overall Accuracy":"95.0"},"paper_url":"https://arxiv.org/abs/2402.17766v3","paper_title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","paper_date":"2024-02-27","code_links":[{"title":"qizekun/ShapeLLM","url":"https://github.com/qizekun/ShapeLLM"},{"title":"qizekun/ReCon","url":"https://github.com/qizekun/ReCon"},{"title":"runpeidong/act","url":"https://github.com/runpeidong/act"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83498,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointGPT","metrics":{"Overall Accuracy":"94.9"},"paper_url":null,"paper_title":null,"paper_date":null,"code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83499,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"point2vec","metrics":{"Mean Accuracy":"92.0","Overall Accuracy":"94.8"},"paper_url":"https://arxiv.org/abs/2303.16570v2","paper_title":"Point2Vec for Self-Supervised Representation Learning on Point Clouds","paper_date":"2023-03-29","code_links":[{"title":"kabouzeid/point2vec","url":"https://github.com/kabouzeid/point2vec"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83500,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ULIP + PointMLP","metrics":{"Mean Accuracy":"92.4","Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83501,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ReCon","metrics":{"Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2302.02318v2","paper_title":"Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining","paper_date":"2023-02-05","code_links":[{"title":"qizekun/ReCon","url":"https://github.com/qizekun/ReCon"},{"title":"runpeidong/act","url":"https://github.com/runpeidong/act"},{"title":"aHapBean/PCP-MAE","url":"https://github.com/aHapBean/PCP-MAE"},{"title":"asterisci/point-gcc","url":"https://github.com/asterisci/point-gcc"},{"title":"qizekun/vpp","url":"https://github.com/qizekun/vpp"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83502,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"AsymDSD-B* (no voting)","metrics":{"Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2506.21724v1","paper_title":"Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning","paper_date":"2025-06-26","code_links":[{"title":"RFLeijenaar/AsymDSD","url":"https://github.com/RFLeijenaar/AsymDSD"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83503,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RepSurf-U","metrics":{"FLOPs":"0.81G","Number of params":"1.48M","Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2205.05740v2","paper_title":"Surface Representation for Point Clouds","paper_date":"2022-05-11","code_links":[{"title":"hancyran/RepSurf","url":"https://github.com/hancyran/RepSurf"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83504,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMLP+HyCoRe","metrics":{"Mean Accuracy":"91.9","Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2209.10318v1","paper_title":"Rethinking the compositionality of point clouds through regularization in the hyperbolic space","paper_date":"2022-09-21","code_links":[{"title":"diegovalsesia/hycore","url":"https://github.com/diegovalsesia/hycore"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83505,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMLP","metrics":{"Mean Accuracy":"91.4","Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2202.07123v2","paper_title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","paper_date":"2022-02-15","code_links":[{"title":"ma-xu/pointmlp-pytorch","url":"https://github.com/ma-xu/pointmlp-pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83506,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-FEMAE","metrics":{"Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2312.10726v1","paper_title":"Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders","paper_date":"2023-12-17","code_links":[{"title":"zyh16143998882/aaai24-pointfemae","url":"https://github.com/zyh16143998882/aaai24-pointfemae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83507,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OTMae3D","metrics":{"Overall Accuracy":"94.5"},"paper_url":"https://ieeexplore.ieee.org/document/10815033","paper_title":"Rethinking Masked Representation Learning for 3D Point Cloud Understanding","paper_date":"2024-12-26","code_links":[{"title":"OpenSpaceAI/OTMae3D","url":"https://github.com/OpenSpaceAI/OTMae3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83508,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet2+PointCMT","metrics":{"Mean Accuracy":"91.2","Number of params":"1.62M","Overall Accuracy":"94.4"},"paper_url":"https://arxiv.org/abs/2210.04208v1","paper_title":"Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis","paper_date":"2022-10-09","code_links":[{"title":"yanx27/2dpass","url":"https://github.com/yanx27/2dpass"},{"title":"zhanheshen/pointcmt","url":"https://github.com/zhanheshen/pointcmt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83509,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"IDPT","metrics":{"Overall Accuracy":"94.4"},"paper_url":"https://arxiv.org/abs/2304.07221v2","paper_title":"Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models","paper_date":"2023-04-14","code_links":[{"title":"zyh16143998882/iccv23-idpt","url":"https://github.com/zyh16143998882/iccv23-idpt"},{"title":"zyh16143998882/IDPT","url":"https://github.com/zyh16143998882/IDPT"},{"title":"zyh16143998882/aaai24-pointfemae","url":"https://github.com/zyh16143998882/aaai24-pointfemae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83510,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OTMae3D (w/o Voting)","metrics":{"Overall Accuracy":"94.3"},"paper_url":"https://ieeexplore.ieee.org/document/10815033","paper_title":"Rethinking Masked Representation Learning for 3D Point Cloud Understanding","paper_date":"2024-12-26","code_links":[{"title":"OpenSpaceAI/OTMae3D","url":"https://github.com/OpenSpaceAI/OTMae3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83511,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"IAE + DGCNN","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2201.00785v5","paper_title":"Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning","paper_date":"2022-01-03","code_links":[{"title":"simingyan/iae","url":"https://github.com/simingyan/iae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83512,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PTv2","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2210.05666v2","paper_title":"Point Transformer V2: Grouped Vector Attention and Partition-based Pooling","paper_date":"2022-10-11","code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"Pointcept/PointTransformerV2","url":"https://github.com/Pointcept/PointTransformerV2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83513,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"CurveNet","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2105.01288v2","paper_title":"Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis","paper_date":"2021-05-04","code_links":[{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"tiangexiang/CurveNet","url":"https://github.com/tiangexiang/CurveNet"},{"title":"RF5/CurveNet","url":"https://github.com/RF5/CurveNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83514,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ExpPoint-MAE","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2306.10798v3","paper_title":"ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers","paper_date":"2023-06-19","code_links":[{"title":"vvrpanda/exppoint-mae","url":"https://github.com/vvrpanda/exppoint-mae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83515,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PCP-MAE","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2408.08753v2","paper_title":"PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders","paper_date":"2024-08-16","code_links":[{"title":"aHapBean/PCP-MAE","url":"https://github.com/aHapBean/PCP-MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83516,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RPNet","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2108.12468v1","paper_title":"Learning Inner-Group Relations on Point Clouds","paper_date":"2021-08-27","code_links":[{"title":"hancyran/RPNet","url":"https://github.com/hancyran/RPNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83517,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ULIP + PointBERT","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83518,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-RAE","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2310.03670v1","paper_title":"Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning","paper_date":"2023-09-25","code_links":[{"title":"liuyyy111/point-rae","url":"https://github.com/liuyyy111/point-rae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83519,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-JEPA (voting)","metrics":{"Overall Accuracy":"94.1±0.1"},"paper_url":"https://arxiv.org/abs/2404.16432v5","paper_title":"Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud","paper_date":"2024-04-25","code_links":[{"title":"Ayumu-J-S/Point-JEPA","url":"https://github.com/Ayumu-J-S/Point-JEPA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83520,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DeLA","metrics":{"FLOPs":"1.44G","Mean Accuracy":"92.2","Number of params":"5.3M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2308.16532v1","paper_title":"Decoupled Local Aggregation for Point Cloud Learning","paper_date":"2023-08-31","code_links":[{"title":"matrix-asc/dela","url":"https://github.com/matrix-asc/dela"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83521,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"P2P","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2208.02812v2","paper_title":"P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting","paper_date":"2022-08-04","code_links":[{"title":"wangzy22/P2P","url":"https://github.com/wangzy22/P2P"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83522,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNeXt","metrics":{"FLOPs":"6.5G","Mean Accuracy":"91.1","Number of params":"4.5M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2206.04670v2","paper_title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","paper_date":"2022-06-09","code_links":[{"title":"guochengqian/pointnext","url":"https://github.com/guochengqian/pointnext"},{"title":"linhaojia13/pointmetabase","url":"https://github.com/linhaojia13/pointmetabase"},{"title":"boyden/pointtransformerfl","url":"https://github.com/boyden/pointtransformerfl"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83523,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point Voxel Transformer","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2108.06076v4","paper_title":"PVT: Point-Voxel Transformer for Point Cloud Learning","paper_date":"2021-08-13","code_links":[{"title":"HaochengWan/PVT","url":"https://github.com/HaochengWan/PVT"},{"title":"Mind23-2/MindCode-109","url":"https://github.com/Mind23-2/MindCode-109"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83524,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-MAE","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2203.06604v2","paper_title":"Masked Autoencoders for Point Cloud Self-supervised Learning","paper_date":"2022-03-13","code_links":[{"title":"Pang-Yatian/Point-MAE","url":"https://github.com/Pang-Yatian/Point-MAE"},{"title":"lmd0311/pointmamba","url":"https://github.com/lmd0311/pointmamba"},{"title":"vvrpanda/exppoint-mae","url":"https://github.com/vvrpanda/exppoint-mae"},{"title":"liujia99/tpm","url":"https://github.com/liujia99/tpm"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83525,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-M2AE","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2205.14401v2","paper_title":"Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training","paper_date":"2022-05-28","code_links":[{"title":"zrrskywalker/i2p-mae","url":"https://github.com/zrrskywalker/i2p-mae"},{"title":"zrrskywalker/point-m2ae","url":"https://github.com/zrrskywalker/point-m2ae"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83526,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMLS","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2401.08210v1","paper_title":"ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification","paper_date":"2024-01-16","code_links":[{"title":"fanglaosi/pointmls","url":"https://github.com/fanglaosi/pointmls"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83527,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3D-JEPA","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2409.15803v1","paper_title":"3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning","paper_date":"2024-09-24","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83528,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"MKConv","metrics":{"Number of params":"5.63M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2107.12655v3","paper_title":"MKConv: Multidimensional Feature Representation for Point Cloud Analysis","paper_date":"2021-07-27","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83529,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SimpleView","metrics":{"Mean Accuracy":"91.8","Overall Accuracy":"93.9"},"paper_url":"https://openreview.net/forum?id=XwATtbX3oCz","paper_title":"Revisiting Point Cloud Classification with a Simple and Effective Baseline","paper_date":"2021-01-01","code_links":[{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"IsaacCorley/simpleview-pytorch","url":"https://github.com/IsaacCorley/simpleview-pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83530,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PAConv","metrics":{"Overall Accuracy":"93.9"},"paper_url":"https://arxiv.org/abs/2103.14635v2","paper_title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","paper_date":"2021-03-26","code_links":[{"title":"PaddlePaddle/Paddle3D","url":"https://github.com/PaddlePaddle/Paddle3D"},{"title":"CVMI-Lab/PAConv","url":"https://github.com/CVMI-Lab/PAConv"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83531,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SimpleView-DGCNN","metrics":{"Overall Accuracy":"93.9"},"paper_url":"https://arxiv.org/abs/2106.05304v1","paper_title":"Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline","paper_date":"2021-06-09","code_links":[{"title":"jiachens/ModelNet40-C","url":"https://github.com/jiachens/ModelNet40-C"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"axeber01/point-tnt","url":"https://github.com/axeber01/point-tnt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83532,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMAE+PPT","metrics":{"Overall Accuracy":"93.88"},"paper_url":"https://arxiv.org/abs/2408.11567v1","paper_title":"Positional Prompt Tuning for Efficient 3D Representation Learning","paper_date":"2024-08-21","code_links":[{"title":"zsc000722/ppt","url":"https://github.com/zsc000722/ppt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83533,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"MVTN","metrics":{"Mean Accuracy":"92.2","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2011.13244v3","paper_title":"MVTN: Multi-View Transformation Network for 3D Shape Recognition","paper_date":"2020-11-26","code_links":[{"title":"ajhamdi/MVTN","url":"https://github.com/ajhamdi/MVTN"},{"title":"ajhamdi/mvtorch","url":"https://github.com/ajhamdi/mvtorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83534,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Feature Geometric Net (FG-Net)","metrics":{"Mean Accuracy":"91.1","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2012.09439v2","paper_title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling","paper_date":"2020-12-17","code_links":[{"title":"KangchengLiu/Feature-Geometric-Net-FG-Net","url":"https://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83535,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"GBNet","metrics":{"Mean Accuracy":"91.0","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/1911.12885v5","paper_title":"Geometric Back-projection Network for Point Cloud Classification","paper_date":"2019-11-28","code_links":[{"title":"ShiQiu0419/GFNet","url":"https://github.com/ShiQiu0419/GFNet"},{"title":"ShiQiu0419/GBNet","url":"https://github.com/ShiQiu0419/GBNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83536,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DeltaConv","metrics":{"Mean class accuracy":"91.2","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2111.08799v5","paper_title":"DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds","paper_date":"2021-11-16","code_links":[{"title":"rubenwiersma/deltaconv","url":"https://github.com/rubenwiersma/deltaconv"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83537,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"GDANet","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2012.10921v3","paper_title":"Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud","paper_date":"2020-12-20","code_links":[{"title":"mutianxu/GDANet","url":"https://github.com/mutianxu/GDANet"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83538,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-BERT","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2111.14819v2","paper_title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","paper_date":"2021-11-29","code_links":[{"title":"julie-tang00/point-bert","url":"https://github.com/julie-tang00/point-bert"},{"title":"lulutang0608/Point-BERT","url":"https://github.com/lulutang0608/Point-BERT"},{"title":"lmd0311/pointmamba","url":"https://github.com/lmd0311/pointmamba"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83539,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"APES (global-based downsample)","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2302.14673v2","paper_title":"Attention-based Point Cloud Edge Sampling","paper_date":"2023-02-28","code_links":[{"title":"JunweiZheng93/APES","url":"https://github.com/JunweiZheng93/APES"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83540,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-JEPA (no voting)","metrics":{"Overall Accuracy":"93.8±0.2"},"paper_url":"https://arxiv.org/abs/2404.16432v5","paper_title":"Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud","paper_date":"2024-04-25","code_links":[{"title":"Ayumu-J-S/Point-JEPA","url":"https://github.com/Ayumu-J-S/Point-JEPA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83541,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-PN","metrics":{"Number of params":"0.8M","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2303.08134v2","paper_title":"Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis","paper_date":"2023-03-14","code_links":[{"title":"zrrskywalker/point-nn","url":"https://github.com/zrrskywalker/point-nn"},{"title":"opengvlab/cafo","url":"https://github.com/opengvlab/cafo"},{"title":"asalarpour/Point_GN","url":"https://github.com/asalarpour/Point_GN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83542,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointSCNet","metrics":{"Mean Accuracy":"91.4","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2202.10251v2","paper_title":"PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling","paper_date":"2022-02-21","code_links":[{"title":"Chenguoz/PointSCNet","url":"https://github.com/Chenguoz/PointSCNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83543,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PRA-Net","metrics":{"Mean Accuracy":"91.2","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2112.04903v1","paper_title":"PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis","paper_date":"2021-12-09","code_links":[{"title":"xiwuchen/pra-net","url":"https://github.com/xiwuchen/pra-net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83544,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointTransformer","metrics":{"Mean Accuracy":"90.6","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2012.09164v2","paper_title":"Point Transformer","paper_date":"2020-12-16","code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"lucidrains/point-transformer-pytorch","url":"https://github.com/lucidrains/point-transformer-pytorch"},{"title":"POSTECH-CVLab/point-transformer","url":"https://github.com/POSTECH-CVLab/point-transformer"},{"title":"gofinge/pointtransformerv2","url":"https://github.com/gofinge/pointtransformerv2"},{"title":"POSTECH-CVLab/FastPointTransformer","url":"https://github.com/POSTECH-CVLab/FastPointTransformer"},{"title":"lifebeyondexpectations/eccv22-pointmixer","url":"https://github.com/lifebeyondexpectations/eccv22-pointmixer"},{"title":"engelnico/point-transformer","url":"https://github.com/engelnico/point-transformer"},{"title":"Sharpiless/Point-Transformer-Pytorch","url":"https://github.com/Sharpiless/Point-Transformer-Pytorch"},{"title":"Meowuu7/Point-Transformer","url":"https://github.com/Meowuu7/Point-Transformer"},{"title":"code-implementation1/Code6","url":"https://github.com/code-implementation1/Code6/tree/main/pointtransformer"},{"title":"jxl152/Point-Transformer","url":"https://github.com/jxl152/Point-Transformer"},{"title":"rauleun/point-transformer-tf2","url":"https://github.com/rauleun/point-transformer-tf2"},{"title":"MS-Mind/MS-Code-06","url":"https://github.com/MS-Mind/MS-Code-06/tree/main/pointtransformer"},{"title":"KernelA/pytorch-point-transformer","url":"https://github.com/KernelA/pytorch-point-transformer"},{"title":"tkdguraa/point-transformer-tensorflow","url":"https://github.com/tkdguraa/point-transformer-tensorflow"},{"title":"sh4175515/PointTransformer","url":"https://github.com/sh4175515/PointTransformer"},{"title":"alzmzyy/pointtransformer","url":"https://github.com/alzmzyy/pointtransformer"},{"title":"Mind23-2/MindCode-5","url":"https://github.com/Mind23-2/MindCode-5/tree/main/pointtransformer"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"2023-MindSpore-1/ms-code-215","url":"https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer"},{"title":"2023-MindSpore-1/ms-code-48","url":"https://github.com/2023-MindSpore-1/ms-code-48"},{"title":"MindSpore-paper-code-2/code2","url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83545,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DualMLP","metrics":{"Overall Accuracy":"93.7"},"paper_url":"https://link.springer.com/article/10.1007/s00371-023-03114-3","paper_title":"DualMLP: a two-stream fusion model for 3D point cloud classification","paper_date":"2023-10-10","code_links":[{"title":"snehaputul/DualMLP","url":"https://github.com/snehaputul/DualMLP"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83546,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMixer","metrics":{"Mean Accuracy":"91.4","Number of params":"6.5M","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2111.11187v5","paper_title":"PointMixer: MLP-Mixer for Point Cloud Understanding","paper_date":"2021-11-22","code_links":[{"title":"lifebeyondexpectations/eccv22-pointmixer","url":"https://github.com/lifebeyondexpectations/eccv22-pointmixer"},{"title":"LifeBeyondExpectations/PointMixer","url":"https://github.com/LifeBeyondExpectations/PointMixer"},{"title":"hrisi/tree-species-classification","url":"https://github.com/hrisi/tree-species-classification"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83547,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DeepGCN","metrics":{"Mean Accuracy":"90.9","Number of params":"2.2M","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/1910.06849v3","paper_title":"DeepGCNs: Making GCNs Go as Deep as CNNs","paper_date":"2019-10-15","code_links":[{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb"},{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_arxiv_dgl"},{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch"},{"title":"lightaime/deep_gcns","url":"https://github.com/lightaime/deep_gcns"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83548,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"LCPFormer","metrics":{"Mean Accuracy":"90.7","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2210.12755v2","paper_title":"LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers","paper_date":"2022-10-23","code_links":[{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83549,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DGCNN + SageMix","metrics":{"Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83550,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointVector-S","metrics":{"Mean Accuracy":"91","Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2205.10528v3","paper_title":"PointVector: A Vector Representation In Point Cloud Analysis","paper_date":"2022-05-21","code_links":[{"title":"guochengqian/openpoints","url":"https://github.com/guochengqian/openpoints/blob/master/models/backbone/pointvector.py"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83551,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RSMix","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2102.01929v3","paper_title":"Regularization Strategy for Point Cloud via Rigidly Mixed Sample","paper_date":"2021-02-03","code_links":[{"title":"dogyoonlee/RSMix-official","url":"https://github.com/dogyoonlee/RSMix-official"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83552,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointConT","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2303.04599v1","paper_title":"Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space","paper_date":"2023-03-08","code_links":[{"title":"yahuiliu99/pointcont","url":"https://github.com/yahuiliu99/pointcont"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83553,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"APES (local-based downsample)","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2302.14673v2","paper_title":"Attention-based Point Cloud Edge Sampling","paper_date":"2023-02-28","code_links":[{"title":"JunweiZheng93/APES","url":"https://github.com/JunweiZheng93/APES"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83554,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DSPoint","metrics":{"Number of params":"1.16M","Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2111.10332v4","paper_title":"DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion","paper_date":"2021-11-19","code_links":[{"title":"adonis-galaxy/dspoint","url":"https://github.com/adonis-galaxy/dspoint"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83555,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ULIP + PointNet++(ssg)","metrics":{"Mean Accuracy":"91.2","Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83556,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointCutmix","metrics":{"Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2101.01461v2","paper_title":"PointCutMix: Regularization Strategy for Point Cloud Classification","paper_date":"2021-01-05","code_links":[{"title":"jiachens/ModelNet40-C","url":"https://github.com/jiachens/ModelNet40-C"},{"title":"cuge1995/PointCutMix","url":"https://github.com/cuge1995/PointCutMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83557,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3DMedPT","metrics":{"Number of params":"1.54M","Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2112.04863v2","paper_title":"3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis","paper_date":"2021-12-09","code_links":[{"title":"crane-papercode/3dmedpt","url":"https://github.com/crane-papercode/3dmedpt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83558,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DGCNN + MD","metrics":{"Mean Accuracy":"89.88","Overall Accuracy":"93.39"},"paper_url":"https://arxiv.org/abs/2109.00452v3","paper_title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","paper_date":"2021-09-01","code_links":[{"title":"cyysc1998/3D-Pretraining","url":"https://github.com/cyysc1998/3D-Pretraining"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83559,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OGNet + MD","metrics":{"Mean Accuracy":"90.71","Overall Accuracy":"93.31"},"paper_url":"https://arxiv.org/abs/2109.00452v3","paper_title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","paper_date":"2021-09-01","code_links":[{"title":"cyysc1998/3D-Pretraining","url":"https://github.com/cyysc1998/3D-Pretraining"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83560,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OG-Net-Small","metrics":{"Mean Accuracy":"90.5","Number of params":"1.22M","Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2006.04569v3","paper_title":"Parameter-Efficient Person Re-identification in the 3D Space","paper_date":"2020-06-08","code_links":[{"title":"layumi/person-reid-3d","url":"https://github.com/layumi/person-reid-3d"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83561,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointStack","metrics":{"Mean Accuracy":"89.6","Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2205.09962v1","paper_title":"Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling","paper_date":"2022-05-20","code_links":[{"title":"LongerVision/PointStack","url":"https://github.com/LongerVision/PointStack"},{"title":"kaist-avelab/PointStack","url":"https://github.com/kaist-avelab/PointStack"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83562,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet++ + SageMix","metrics":{"Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83563,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointASNL","metrics":{"Overall Accuracy":"93.2"},"paper_url":"https://arxiv.org/abs/2003.00492v3","paper_title":"PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling","paper_date":"2020-03-01","code_links":[{"title":"yanx27/PointASNL","url":"https://github.com/yanx27/PointASNL"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83564,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point Cloud Transformer","metrics":{"Number of params":"2.88M","Overall Accuracy":"93.2"},"paper_url":"https://arxiv.org/abs/2012.09688v4","paper_title":"PCT: Point cloud transformer","paper_date":"2020-12-17","code_links":[{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"MenghaoGuo/PCT","url":"https://github.com/MenghaoGuo/PCT"},{"title":"Strawberry-Eat-Mango/PCT_Pytorch","url":"https://github.com/Strawberry-Eat-Mango/PCT_Pytorch"},{"title":"uyzhang/PCT_Pytorch","url":"https://github.com/uyzhang/PCT_Pytorch"},{"title":"uuyzhang/PCT_Pytorch","url":"https://github.com/uuyzhang/PCT_Pytorch"},{"title":"irmvlab/point-mamba","url":"https://github.com/irmvlab/point-mamba"},{"title":"qinglew/PointCloudTransformer","url":"https://github.com/qinglew/PointCloudTransformer"},{"title":"Liu-Feng/PCT-tensorflow","url":"https://github.com/Liu-Feng/PCT-tensorflow"},{"title":"ViniciusMikuni/PCT_HEP","url":"https://github.com/ViniciusMikuni/PCT_HEP"},{"title":"toothfairy42/PCT-pytorch","url":"https://github.com/toothfairy42/PCT-pytorch"},{"title":"DLW3D/PCT_Pytorch","url":"https://github.com/DLW3D/PCT_Pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83565,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DRNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/2005.06734v2","paper_title":"Dense-Resolution Network for Point Cloud Classification and Segmentation","paper_date":"2020-05-14","code_links":[{"title":"ShiQiu0419/DRNet","url":"https://github.com/ShiQiu0419/DRNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83566,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ShellNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/1908.06295v1","paper_title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","paper_date":"2019-08-17","code_links":[{"title":"hkust-vgd/shellnet","url":"https://github.com/hkust-vgd/shellnet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83567,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"STRL + DGCNN","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/2109.00179v1","paper_title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","paper_date":"2021-09-01","code_links":[{"title":"yichen928/STRL","url":"https://github.com/yichen928/STRL"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83568,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PatchAugment+DGCNN","metrics":{"Overall Accuracy":"93.1"},"paper_url":null,"paper_title":null,"paper_date":null,"code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83569,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"AdaCrossNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://inass.org/wp-content/uploads/2024/10/2025022911-2.pdf","paper_title":"AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning","paper_date":"2025-01-02","code_links":[{"title":"virgantara/AdaCrossNet","url":"https://github.com/virgantara/AdaCrossNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83570,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointManifold","metrics":{"Mean Accuracy":"90.4","Overall Accuracy":"93.0"},"paper_url":"https://arxiv.org/abs/2010.07215v2","paper_title":"PointManifold: Using Manifold Learning for Point Cloud Classification","paper_date":"2020-10-14","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83571,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"InterpCNN","metrics":{"Overall Accuracy":"93.0"},"paper_url":"https://arxiv.org/abs/1908.04512v1","paper_title":"Interpolated Convolutional Networks for 3D Point Cloud Understanding","paper_date":"2019-08-13","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83572,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DGCNN","metrics":{"Mean Accuracy":"90.2","Number of params":"1.81M","Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1801.07829v2","paper_title":"Dynamic Graph CNN for Learning on Point Clouds","paper_date":"2018-01-24","code_links":[{"title":"WangYueFt/dgcnn","url":"https://github.com/WangYueFt/dgcnn"},{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"AnTao97/dgcnn.pytorch","url":"https://github.com/AnTao97/dgcnn.pytorch"},{"title":"bupt-gamma/gammagl","url":"https://github.com/bupt-gamma/gammagl"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"hqucms/ParticleNet","url":"https://github.com/hqucms/ParticleNet"},{"title":"lingzhang1/ContrastNet","url":"https://github.com/lingzhang1/ContrastNet"},{"title":"cy-xu/spatially_aware_ai","url":"https://github.com/cy-xu/spatially_aware_ai"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"},{"title":"hansen7/NRS_3D","url":"https://github.com/hansen7/NRS_3D"},{"title":"brent-murray/tr3d_pointaugdgcnn","url":"https://github.com/brent-murray/tr3d_pointaugdgcnn"},{"title":"nnn911/MLSI","url":"https://github.com/nnn911/MLSI"},{"title":"lingzhang1/dgcnn_sampling","url":"https://github.com/lingzhang1/dgcnn_sampling"},{"title":"lingzhang1/dgcnn_v2","url":"https://github.com/lingzhang1/dgcnn_v2"},{"title":"xrndai/dgcnn_amino_acid_structure","url":"https://github.com/xrndai/dgcnn_amino_acid_structure"},{"title":"JingfeiHuang/DGCNN-Paddle","url":"https://github.com/JingfeiHuang/DGCNN-Paddle"},{"title":"af13s/dgcnn-amino","url":"https://github.com/af13s/dgcnn-amino"},{"title":"Squanch-U/DGCNN","url":"https://github.com/Squanch-U/DGCNN"},{"title":"AmitBracha/GIP_project","url":"https://github.com/AmitBracha/GIP_project"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83573,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"KPConv","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1904.08889v2","paper_title":"KPConv: Flexible and Deformable Convolution for Point Clouds","paper_date":"2019-04-18","code_links":[{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"HuguesTHOMAS/KPConv-PyTorch","url":"https://github.com/HuguesTHOMAS/KPConv-PyTorch"},{"title":"HuguesTHOMAS/KPConv","url":"https://github.com/HuguesTHOMAS/KPConv"},{"title":"ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D"},{"title":"XuyangBai/KPConv.pytorch","url":"https://github.com/XuyangBai/KPConv.pytorch"},{"title":"plusmultiply/mprm","url":"https://github.com/plusmultiply/mprm"},{"title":"Arjun-NA/KPConv_for_DALES","url":"https://github.com/Arjun-NA/KPConv_for_DALES"},{"title":"Yacovitch/EyeNet","url":"https://github.com/Yacovitch/EyeNet"},{"title":"genglinliu/KPConv_Pytorch","url":"https://github.com/genglinliu/KPConv_Pytorch"},{"title":"JohnRomanelis/KPConv_torch_geometric","url":"https://github.com/JohnRomanelis/KPConv_torch_geometric"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83574,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RS-CNN","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1904.07601v3","paper_title":"Relation-Shape Convolutional Neural Network for Point Cloud Analysis","paper_date":"2019-04-16","code_links":[{"title":"Yochengliu/Relation-Shape-CNN","url":"https://github.com/Yochengliu/Relation-Shape-CNN"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"sausagecy/RSCNN_Pytorch1.0","url":"https://github.com/sausagecy/RSCNN_Pytorch1.0"},{"title":"panyunyi97/RSCNN","url":"https://github.com/panyunyi97/RSCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83575,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ASSANet","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/2110.10538v2","paper_title":"ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning","paper_date":"2021-10-20","code_links":[{"title":"guochengqian/assanet","url":"https://github.com/guochengqian/assanet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83576,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-M2AE-SVM","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/2205.14401v2","paper_title":"Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training","paper_date":"2022-05-28","code_links":[{"title":"zrrskywalker/i2p-mae","url":"https://github.com/zrrskywalker/i2p-mae"},{"title":"zrrskywalker/point-m2ae","url":"https://github.com/zrrskywalker/point-m2ae"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83577,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point Transformer","metrics":{"Overall Accuracy":"92.8"},"paper_url":"https://arxiv.org/abs/2011.00931v2","paper_title":"Point Transformer","paper_date":"2020-11-02","code_links":[{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"engelnico/point-transformer","url":"https://github.com/engelnico/point-transformer"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83578,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"A-CNN","metrics":{"Overall Accuracy":"92.6"},"paper_url":"http://arxiv.org/abs/1904.08017v1","paper_title":"A-CNN: Annularly Convolutional Neural Networks on Point Clouds","paper_date":"2019-04-16","code_links":[{"title":"artemkomarichev/a-cnn","url":"https://github.com/artemkomarichev/a-cnn"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83579,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"P2Sequence","metrics":{"Overall Accuracy":"92.6"},"paper_url":"http://arxiv.org/abs/1811.02565v2","paper_title":"Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network","paper_date":"2018-11-06","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83580,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-TnT","metrics":{"Number of params":"3.9M","Overall Accuracy":"92.6"},"paper_url":"https://arxiv.org/abs/2204.03957v1","paper_title":"Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition","paper_date":"2022-04-08","code_links":[{"title":"axeber01/point-tnt","url":"https://github.com/axeber01/point-tnt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83581,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointConv","metrics":{"Overall Accuracy":"92.5"},"paper_url":"https://arxiv.org/abs/1811.07246v3","paper_title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","paper_date":"2018-11-17","code_links":[{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"DylanWusee/pointconv","url":"https://github.com/DylanWusee/pointconv"},{"title":"DylanWusee/pointconv_pytorch","url":"https://github.com/DylanWusee/pointconv_pytorch"},{"title":"dgriffiths3/pointconv-tensorflow2","url":"https://github.com/dgriffiths3/pointconv-tensorflow2"},{"title":"Young98CN/pointconv_pytorch","url":"https://github.com/Young98CN/pointconv_pytorch"},{"title":"Ghailen-Ben-Achour/PointConv_segmentation","url":"https://github.com/Ghailen-Ben-Achour/PointConv_segmentation"},{"title":"koritsky/pointconv","url":"https://github.com/koritsky/pointconv"},{"title":"THHHomas/mls","url":"https://github.com/THHHomas/mls"},{"title":"ECE685-FinalProject/3D-object-recognition","url":"https://github.com/ECE685-FinalProject/3D-object-recognition"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83582,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PolyNet","metrics":{"Overall Accuracy":"92.42"},"paper_url":"https://arxiv.org/abs/2110.07882v1","paper_title":"PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation","paper_date":"2021-10-15","code_links":[{"title":"myavartanoo/PolyNet_PyTorch","url":"https://github.com/myavartanoo/PolyNet_PyTorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83583,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SpiderCNN","metrics":{"Overall Accuracy":"92.4"},"paper_url":"http://arxiv.org/abs/1803.11527v3","paper_title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","paper_date":"2018-03-30","code_links":[{"title":"xyf513/SpiderCNN","url":"https://github.com/xyf513/SpiderCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83584,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PCNN","metrics":{"Overall Accuracy":"92.3"},"paper_url":"http://arxiv.org/abs/1803.10091v1","paper_title":"Point Convolutional Neural Networks by Extension Operators","paper_date":"2018-03-27","code_links":[{"title":"matanatz/pcnn","url":"https://github.com/matanatz/pcnn"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83585,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointCNN","metrics":{"Overall Accuracy":"92.2"},"paper_url":"http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points","paper_title":"PointCNN: Convolution On X-Transformed Points","paper_date":"2018-12-01","code_links":[{"title":"yangyanli/PointCNN","url":"https://github.com/yangyanli/PointCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83586,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-PlaneNet","metrics":{"Mean Accuracy":"90.5","Overall Accuracy":"92.1"},"paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S1051200419301873","paper_title":"Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis","paper_date":"2020-05-01","code_links":[{"title":"moeinp70/Point-Planenet","url":"https://github.com/moeinp70/Point-Planenet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83587,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SpecGCN","metrics":{"Overall Accuracy":"92.1"},"paper_url":"http://arxiv.org/abs/1803.05827v1","paper_title":"Local Spectral Graph Convolution for Point Set Feature Learning","paper_date":"2018-03-15","code_links":[{"title":"fate3439/LocalSpecGCN","url":"https://github.com/fate3439/LocalSpecGCN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83588,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DynamicScale","metrics":{"Overall Accuracy":"92.1"},"paper_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9565556","paper_title":"Dynamic Local Geometry Capture in 3D PointCloud Classification","paper_date":"2021-10-19","code_links":[{"title":"VimsLab/DynamicScale","url":"https://github.com/VimsLab/DynamicScale"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83589,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointGrid","metrics":{"Overall Accuracy":"92.0"},"paper_url":"http://openaccess.thecvf.com/content_cvpr_2018/html/Le_PointGrid_A_Deep_CVPR_2018_paper.html","paper_title":"PointGrid: A Deep Network for 3D Shape Understanding","paper_date":"2018-06-01","code_links":[{"title":"trucleduc/PointGrid","url":"https://github.com/trucleduc/PointGrid"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83590,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Kd-Net","metrics":{"Overall Accuracy":"91.8"},"paper_url":"http://arxiv.org/abs/1704.01222v2","paper_title":"Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models","paper_date":"2017-04-04","code_links":[{"title":"fxia22/kdnet.pytorch","url":"https://github.com/fxia22/kdnet.pytorch"},{"title":"qq456cvb/KdNet-Tensorflow","url":"https://github.com/qq456cvb/KdNet-Tensorflow"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83591,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Overall Accuracy":"91.7"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/8354322","paper_title":"General-Purpose Deep Point Cloud Feature Extractor","paper_date":"2018-03-12","code_links":[{"title":"WDot/G3DNet","url":"https://github.com/WDot/G3DNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83592,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3DMFV-Net","metrics":{"Overall Accuracy":"91.6"},"paper_url":"http://arxiv.org/abs/1711.08241v1","paper_title":"3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks","paper_date":"2017-11-22","code_links":[{"title":"sitzikbs/3DmFV-Net","url":"https://github.com/sitzikbs/3DmFV-Net"},{"title":"sitzikbs/3DmFV-Net-MATLAB","url":"https://github.com/sitzikbs/3DmFV-Net-MATLAB"},{"title":"fferroni/fisher_vector_classifier","url":"https://github.com/fferroni/fisher_vector_classifier"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83593,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Overall Accuracy":"91.49"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/10229841","paper_title":"CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud","paper_date":"2023-06-08","code_links":[{"title":"snehaputul/CrossMoCo","url":"https://github.com/snehaputul/CrossMoCo"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83594,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SO-Net","metrics":{"Overall Accuracy":"90.9"},"paper_url":"http://arxiv.org/abs/1803.04249v4","paper_title":"SO-Net: Self-Organizing Network for Point Cloud Analysis","paper_date":"2018-03-12","code_links":[{"title":"lijx10/SO-Net","url":"https://github.com/lijx10/SO-Net"},{"title":"donnyruixu/pc-elm-ae","url":"https://github.com/donnyruixu/pc-elm-ae"},{"title":"LONG-9621/SO-Net","url":"https://github.com/LONG-9621/SO-Net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83595,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet++","metrics":{"Number of params":"1.74M","Overall Accuracy":"90.7"},"paper_url":"http://arxiv.org/abs/1706.02413v1","paper_title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","paper_date":"2017-06-07","code_links":[{"title":"yanx27/Pointnet_Pointnet2_pytorch","url":"https://github.com/yanx27/Pointnet_Pointnet2_pytorch"},{"title":"charlesq34/pointnet2","url":"https://github.com/charlesq34/pointnet2"},{"title":"facebookresearch/votenet","url":"https://github.com/facebookresearch/votenet"},{"title":"erikwijmans/Pointnet2_PyTorch","url":"https://github.com/erikwijmans/Pointnet2_PyTorch"},{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"sshaoshuai/Pointnet2.PyTorch","url":"https://github.com/sshaoshuai/Pointnet2.PyTorch"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/pointnet2"},{"title":"zaiweizhang/H3DNet","url":"https://github.com/zaiweizhang/H3DNet"},{"title":"hehefan/P4Transformer","url":"https://github.com/hehefan/P4Transformer"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"thu17cyz/3DIoUMatch","url":"https://github.com/thu17cyz/3DIoUMatch"},{"title":"hehefan/PointRNN","url":"https://github.com/hehefan/PointRNN"},{"title":"facebookresearch/imvotenet","url":"https://github.com/facebookresearch/imvotenet"},{"title":"referit3d/referit3d","url":"https://github.com/referit3d/referit3d"},{"title":"hehefan/Point-Spatio-Temporal-Convolution","url":"https://github.com/hehefan/Point-Spatio-Temporal-Convolution"},{"title":"dgriffiths3/pointnet2-tensorflow2","url":"https://github.com/dgriffiths3/pointnet2-tensorflow2"},{"title":"nickgkan/beauty_detr","url":"https://github.com/nickgkan/beauty_detr"},{"title":"nickgkan/butd_detr","url":"https://github.com/nickgkan/butd_detr"},{"title":"open-air-sun/pq-transformer","url":"https://github.com/open-air-sun/pq-transformer"},{"title":"hehefan/PointRNN-PyTorch","url":"https://github.com/hehefan/PointRNN-PyTorch"},{"title":"joyhsu0504/ns3d","url":"https://github.com/joyhsu0504/ns3d"},{"title":"curryyuan/x-trans2cap","url":"https://github.com/curryyuan/x-trans2cap"},{"title":"zyang-ur/SAT","url":"https://github.com/zyang-ur/SAT"},{"title":"iballester/SPiKE","url":"https://github.com/iballester/SPiKE"},{"title":"johnsonsign/mast-pre","url":"https://github.com/johnsonsign/mast-pre"},{"title":"AsahiLiu/PointDetectron","url":"https://github.com/AsahiLiu/PointDetectron"},{"title":"hehefan/PSTNet2","url":"https://github.com/hehefan/PSTNet2"},{"title":"JohnsonSign/PointCMP","url":"https://github.com/JohnsonSign/PointCMP"},{"title":"dfki-av/mikasa-3dvg","url":"https://github.com/dfki-av/mikasa-3dvg"},{"title":"Ghailen-Ben-Achour/PointNet2_Segmentation","url":"https://github.com/Ghailen-Ben-Achour/PointNet2_Segmentation"},{"title":"houseleo/pointnet","url":"https://github.com/houseleo/pointnet"},{"title":"tao-tao-tao-tao-tao/diffusion_suction","url":"https://github.com/tao-tao-tao-tao-tao/diffusion_suction"},{"title":"Lonepic/SPIB","url":"https://github.com/Lonepic/SPIB"},{"title":"asalarpour/Point_GN","url":"https://github.com/asalarpour/Point_GN"},{"title":"LinZhuoChen/pointnet2_multi_gpu","url":"https://github.com/LinZhuoChen/pointnet2_multi_gpu"},{"title":"shaygu62/Protein-Classification","url":"https://github.com/shaygu62/Protein-Classification"},{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"},{"title":"ikh-innovation/roboweldar-votenet","url":"https://github.com/ikh-innovation/roboweldar-votenet"},{"title":"witignite/Frustum-PointNet","url":"https://github.com/witignite/Frustum-PointNet"},{"title":"airalcorn2/parking-lot-pointnetplusplus","url":"https://github.com/airalcorn2/parking-lot-pointnetplusplus"},{"title":"ftdlyc/pointnet_pytorch","url":"https://github.com/ftdlyc/pointnet_pytorch"},{"title":"tingxueronghua/dpke","url":"https://github.com/tingxueronghua/dpke"},{"title":"timothylimyl/PointNet-Pytorch","url":"https://github.com/timothylimyl/PointNet-Pytorch"},{"title":"zhh6425/MotionPointNet","url":"https://github.com/zhh6425/MotionPointNet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/ascend_pointnet2"},{"title":"hoangcuongbk80/VoteGrasp","url":"https://github.com/hoangcuongbk80/VoteGrasp"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet2"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/PointNet2"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/PointNet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/gpu_pointnet2"},{"title":"tonysy/pointnet2_tf","url":"https://github.com/tonysy/pointnet2_tf"},{"title":"Xiangxu-0103/Octant-CNN","url":"https://github.com/Xiangxu-0103/Octant-CNN"},{"title":"Lw510107/PointNet","url":"https://github.com/Lw510107/PointNet"},{"title":"xurui1217/pointnet2-master","url":"https://github.com/xurui1217/pointnet2-master"},{"title":"SY-Xuan/pointnet_plus_plus_paddlepaddle","url":"https://github.com/SY-Xuan/pointnet_plus_plus_paddlepaddle"},{"title":"LONG-9621/VoteNet","url":"https://github.com/LONG-9621/VoteNet"},{"title":"LONG-9621/PointNet-","url":"https://github.com/LONG-9621/PointNet-"},{"title":"Harut0726/votenet","url":"https://github.com/Harut0726/votenet"},{"title":"FlowWind1999/pointnet-2","url":"https://github.com/FlowWind1999/pointnet-2"},{"title":"llzlcl/pointcloud-segment","url":"https://github.com/llzlcl/pointcloud-segment"},{"title":"zenroad/modifypointnet","url":"https://github.com/zenroad/modifypointnet"},{"title":"caizeyu1992/pointnet2","url":"https://github.com/caizeyu1992/pointnet2"},{"title":"2023-MindSpore-1/ms-code-8","url":"https://github.com/2023-MindSpore-1/ms-code-8/tree/main/PointNet2"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"2023-MindSpore-1/ms-code-223","url":"https://github.com/2023-MindSpore-1/ms-code-223/tree/main/PointNet"},{"title":"MindSpore-paper-code-3/code4","url":"https://github.com/MindSpore-paper-code-3/code4/tree/main/frustum-pointnet"},{"title":"2023-MindSpore-1/ms-code-223","url":"https://github.com/2023-MindSpore-1/ms-code-223/tree/main/PointNet2"},{"title":"brbzjl/pointnet2","url":"https://github.com/brbzjl/pointnet2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83596,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Kd-net","metrics":{"Overall Accuracy":"90.6"},"paper_url":"http://arxiv.org/abs/1704.01222v2","paper_title":"Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models","paper_date":"2017-04-04","code_links":[{"title":"fxia22/kdnet.pytorch","url":"https://github.com/fxia22/kdnet.pytorch"},{"title":"qq456cvb/KdNet-Tensorflow","url":"https://github.com/qq456cvb/KdNet-Tensorflow"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83597,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet + SageMix","metrics":{"Overall Accuracy":"90.3"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83598,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"MVCNN","metrics":{"Overall Accuracy":"90.1"},"paper_url":"http://arxiv.org/abs/1505.00880v3","paper_title":"Multi-view Convolutional Neural Networks for 3D Shape Recognition","paper_date":"2015-05-05","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83599,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet","metrics":{"Mean Accuracy":"86.0","Number of params":"3.47M","Overall Accuracy":"89.2"},"paper_url":"http://arxiv.org/abs/1612.00593v2","paper_title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","paper_date":"2016-12-02","code_links":[{"title":"charlesq34/pointnet","url":"https://github.com/charlesq34/pointnet"},{"title":"fxia22/pointnet.pytorch","url":"https://github.com/fxia22/pointnet.pytorch"},{"title":"WangYueFt/dgcnn","url":"https://github.com/WangYueFt/dgcnn"},{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"DylanWusee/pointconv_pytorch","url":"https://github.com/DylanWusee/pointconv_pytorch"},{"title":"nikitakaraevv/pointnet","url":"https://github.com/nikitakaraevv/pointnet"},{"title":"maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification","url":"https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"sarthakTUM/roofn3d","url":"https://github.com/sarthakTUM/roofn3d"},{"title":"jtpils/treegan","url":"https://github.com/jtpils/treegan"},{"title":"ajhamdi/AdvPC","url":"https://github.com/ajhamdi/AdvPC"},{"title":"soumik12345/point-cloud-segmentation","url":"https://github.com/soumik12345/point-cloud-segmentation"},{"title":"AI-Guru/pointcloud_experiments","url":"https://github.com/AI-Guru/pointcloud_experiments"},{"title":"ali-stanford/pointnetcfd","url":"https://github.com/ali-stanford/pointnetcfd"},{"title":"romaintha/pytorch_pointnet","url":"https://github.com/romaintha/pytorch_pointnet"},{"title":"saaries/PointNet","url":"https://github.com/saaries/PointNet"},{"title":"Fragjacker/Pointcloud-grad-CAM","url":"https://github.com/Fragjacker/Pointcloud-grad-CAM"},{"title":"ZhihaoZhu/PointNet-Implementation-Tensorflow","url":"https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow"},{"title":"YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti","url":"https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti"},{"title":"Yuto0107/pointnet","url":"https://github.com/Yuto0107/pointnet"},{"title":"YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024","url":"https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024"},{"title":"opeco17/pointnet","url":"https://github.com/opeco17/pointnet"},{"title":"Dir-b/PointNet","url":"https://github.com/Dir-b/PointNet"},{"title":"Young98CN/pointconv_pytorch","url":"https://github.com/Young98CN/pointconv_pytorch"},{"title":"y2kmz/pointnetv2","url":"https://github.com/y2kmz/pointnetv2"},{"title":"YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization","url":"https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization"},{"title":"imanlab/deep_movement_primitives","url":"https://github.com/imanlab/deep_movement_primitives"},{"title":"abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds","url":"https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds"},{"title":"donshen/pointnet.phasedetection","url":"https://github.com/donshen/pointnet.phasedetection"},{"title":"ModelBunker/PointNet-TensorFlow","url":"https://github.com/ModelBunker/PointNet-TensorFlow"},{"title":"amyllykoski/CycleGAN","url":"https://github.com/amyllykoski/CycleGAN"},{"title":"Javed002/Object_detection_PointNet","url":"https://github.com/Javed002/Object_detection_PointNet"},{"title":"sanantoniochili/PointCloud_KNN","url":"https://github.com/sanantoniochili/PointCloud_KNN"},{"title":"PaParaZz1/PointNet","url":"https://github.com/PaParaZz1/PointNet"},{"title":"yevgm/deepadv3d","url":"https://github.com/yevgm/deepadv3d"},{"title":"witignite/Frustum-PointNet","url":"https://github.com/witignite/Frustum-PointNet"},{"title":"m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks","url":"https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks"},{"title":"alpemek/ais3d","url":"https://github.com/alpemek/ais3d"},{"title":"yanxp/PointNet","url":"https://github.com/yanxp/PointNet"},{"title":"ftdlyc/pointnet_pytorch","url":"https://github.com/ftdlyc/pointnet_pytorch"},{"title":"Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification","url":"https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification"},{"title":"Phimos/Paddle-PointNet","url":"https://github.com/Phimos/Paddle-PointNet"},{"title":"zgx0534/pointnet_win","url":"https://github.com/zgx0534/pointnet_win"},{"title":"timothylimyl/PointNet-Pytorch","url":"https://github.com/timothylimyl/PointNet-Pytorch"},{"title":"minhncedutw/pointnet1_keras","url":"https://github.com/minhncedutw/pointnet1_keras"},{"title":"Jagyan/ghost_radarpoint_detection","url":"https://github.com/Jagyan/ghost_radarpoint_detection"},{"title":"brendanmallery9/entropic-barycenters","url":"https://github.com/brendanmallery9/entropic-barycenters"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet"},{"title":"xiuyu0000/vision","url":"https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py"},{"title":"hnmspirit/pointnet1","url":"https://github.com/hnmspirit/pointnet1"},{"title":"yangyucheng000/pointnet","url":"https://github.com/yangyucheng000/pointnet"},{"title":"eth212/Qmind_ModelNet10_PointNet","url":"https://github.com/eth212/Qmind_ModelNet10_PointNet"},{"title":"smfirmin/DeepLearningFinalProject","url":"https://github.com/smfirmin/DeepLearningFinalProject"},{"title":"ECE685-FinalProject/3D-object-recognition","url":"https://github.com/ECE685-FinalProject/3D-object-recognition"},{"title":"KhusDM/PointNetTree","url":"https://github.com/KhusDM/PointNetTree"},{"title":"Lw510107/pointnet-2018.6.27-","url":"https://github.com/Lw510107/pointnet-2018.6.27-"},{"title":"xurui1217/pointnet.pytorch-master","url":"https://github.com/xurui1217/pointnet.pytorch-master"},{"title":"hnVfly/pointnet.mxnet","url":"https://github.com/hnVfly/pointnet.mxnet"},{"title":"LebronGG/PointNet","url":"https://github.com/LebronGG/PointNet"},{"title":"GOD-GOD-Autonomous-Vehicle/self-pointnet","url":"https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet"},{"title":"AlfredoZermini/PointNet","url":"https://github.com/AlfredoZermini/PointNet"},{"title":"wonderland-dsg/pointnet-grid","url":"https://github.com/wonderland-dsg/pointnet-grid"},{"title":"aviros/roatationPointnet","url":"https://github.com/aviros/roatationPointnet"},{"title":"THHHomas/mls","url":"https://github.com/THHHomas/mls"},{"title":"liuch37/pointnet","url":"https://github.com/liuch37/pointnet"},{"title":"yanx27/Pointnet","url":"https://github.com/yanx27/Pointnet"},{"title":"Taeuk-Jang/pointcompletion","url":"https://github.com/Taeuk-Jang/pointcompletion"},{"title":"ytng001/sensemaking","url":"https://github.com/ytng001/sensemaking"},{"title":"nddoshi/pointnet","url":"https://github.com/nddoshi/pointnet"},{"title":"SY-Xuan/pointnet_paddle","url":"https://github.com/SY-Xuan/pointnet_paddle"},{"title":"Cmeijiang/PointNet_MS","url":"https://github.com/Cmeijiang/PointNet_MS"},{"title":"AndreiMoraru123/PointNet","url":"https://github.com/AndreiMoraru123/PointNet"},{"title":"mengxingshifen1218/pointnet.pytorch","url":"https://github.com/mengxingshifen1218/pointnet.pytorch"},{"title":"Moon-Xuezha/PointNet","url":"https://github.com/Moon-Xuezha/PointNet"},{"title":"ajertec/PointNetKeras","url":"https://github.com/ajertec/PointNetKeras"},{"title":"2023-MindSpore-1/ms-code-35","url":"https://github.com/2023-MindSpore-1/ms-code-35"},{"title":"LONG-9621/Extract_Point_3D","url":"https://github.com/LONG-9621/Extract_Point_3D"},{"title":"Mind23-2/MindCode-63","url":"https://github.com/Mind23-2/MindCode-63"},{"title":"merazlab/3D_Deep_Learning_Link","url":"https://github.com/merazlab/3D_Deep_Learning_Link"},{"title":"2023-MindSpore-1/ms-code-7","url":"https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet"},{"title":"KaidongLi/tf-3d-alpha","url":"https://github.com/KaidongLi/tf-3d-alpha"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"prasadsawant5/PointNet","url":"https://github.com/prasadsawant5/PointNet"},{"title":"SonuDileep/3-D-Object-Detection-using-PointNet","url":"https://github.com/SonuDileep/3-D-Object-Detection-using-PointNet"},{"title":"BPMJG/annotated_pointnet","url":"https://github.com/BPMJG/annotated_pointnet"},{"title":"debajyotid2/pointnet_pl","url":"https://github.com/debajyotid2/pointnet_pl"},{"title":"SBPL-Cruz/perch_pose_sampler","url":"https://github.com/SBPL-Cruz/perch_pose_sampler"},{"title":"2023-MindSpore-4/Code9","url":"https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet"},{"title":"wuryantoAji/POINTNET","url":"https://github.com/wuryantoAji/POINTNET"},{"title":"LONG-9621/PointNet","url":"https://github.com/LONG-9621/PointNet"},{"title":"bt77/pointnet","url":"https://github.com/bt77/pointnet"},{"title":"MindSpore-paper-code-3/code9","url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2"},{"title":"KiranAkadas/My_Pointnet_v2","url":"https://github.com/KiranAkadas/My_Pointnet_v2"},{"title":"YiruS/pointnet_adversarial","url":"https://github.com/YiruS/pointnet_adversarial"},{"title":"Harut0726/my-pointnet-tensorflow","url":"https://github.com/Harut0726/my-pointnet-tensorflow"},{"title":"AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation","url":"https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation"},{"title":"hz-ants/pointnet.pytorch","url":"https://github.com/hz-ants/pointnet.pytorch"},{"title":"VaanHUANG/CSCI5210HW1","url":"https://github.com/VaanHUANG/CSCI5210HW1"},{"title":"MindSpore-paper-code-3/code9","url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet"},{"title":"ShadowShadowWong/update-pointnet-work","url":"https://github.com/ShadowShadowWong/update-pointnet-work"},{"title":"Yang2446/pointnet","url":"https://github.com/Yang2446/pointnet"},{"title":"lingzhang1/pointnet_pytorch","url":"https://github.com/lingzhang1/pointnet_pytorch"},{"title":"dwtstore/sfm1","url":"https://github.com/dwtstore/sfm1"},{"title":"freddieee/pn_6d_single","url":"https://github.com/freddieee/pn_6d_single"},{"title":"ahmed-anas/thesis-pointnet","url":"https://github.com/ahmed-anas/thesis-pointnet"},{"title":"coconutzs/PointNet_zs","url":"https://github.com/coconutzs/PointNet_zs"},{"title":"CheesyB/cpointnet","url":"https://github.com/CheesyB/cpointnet"},{"title":"lingzhang1/pointnet_tensorflow","url":"https://github.com/lingzhang1/pointnet_tensorflow"},{"title":"aviros/pointnet_totations","url":"https://github.com/aviros/pointnet_totations"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83600,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Subvolume","metrics":{"Overall Accuracy":"89.2"},"paper_url":"http://arxiv.org/abs/1604.03265v2","paper_title":"Volumetric and Multi-View CNNs for Object Classification on 3D Data","paper_date":"2016-04-12","code_links":[{"title":"charlesq34/3dcnn.torch","url":"https://github.com/charlesq34/3dcnn.torch"},{"title":"LONG-9621/3DCNN","url":"https://github.com/LONG-9621/3DCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83601,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ECC","metrics":{"Mean Accuracy":"83.2","Overall Accuracy":"87.4"},"paper_url":"http://arxiv.org/abs/1704.02901v3","paper_title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","paper_date":"2017-04-10","code_links":[{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"mys007/ecc","url":"https://github.com/mys007/ecc"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83602,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"VRN (multiple views)","metrics":{"Mean Accuracy":"91.33"},"paper_url":"http://arxiv.org/abs/1608.04236v2","paper_title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","paper_date":"2016-08-15","code_links":[{"title":"ajbrock/Generative-and-Discriminative-Voxel-Modeling","url":"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83603,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"VRN (single view)","metrics":{"Mean Accuracy":"88.98"},"paper_url":"http://arxiv.org/abs/1608.04236v2","paper_title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","paper_date":"2016-08-15","code_links":[{"title":"ajbrock/Generative-and-Discriminative-Voxel-Modeling","url":"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83604,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3DShapeNets","metrics":{"Mean Accuracy":"77.3"},"paper_url":"http://arxiv.org/abs/1406.5670v3","paper_title":"3D ShapeNets: A Deep Representation for Volumetric Shapes","paper_date":"2014-06-22","code_links":[{"title":"zhirongw/3DShapeNets","url":"https://github.com/zhirongw/3DShapeNets"},{"title":"hamidreza-dastmalchi/3dd-tta","url":"https://github.com/hamidreza-dastmalchi/3dd-tta"},{"title":"AliBahri94/SVWA_TTA","url":"https://github.com/AliBahri94/SVWA_TTA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83605,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Perceiver","metrics":{"Mean Accuracy":"14.3"},"paper_url":"https://arxiv.org/abs/2103.03206v2","paper_title":"Perceiver: General Perception with Iterative Attention","paper_date":"2021-03-04","code_links":[{"title":"deepmind/deepmind-research","url":"https://github.com/deepmind/deepmind-research/tree/master/perceiver"},{"title":"towhee-io/towhee","url":"https://github.com/towhee-io/towhee"},{"title":"lucidrains/perceiver-pytorch","url":"https://github.com/lucidrains/perceiver-pytorch"},{"title":"krasserm/perceiver-io","url":"https://github.com/krasserm/perceiver-io"},{"title":"Rishit-dagli/Perceiver","url":"https://github.com/Rishit-dagli/Perceiver"},{"title":"louislva/deepmind-perceiver","url":"https://github.com/louislva/deepmind-perceiver"},{"title":"Xianchao-Wu/perceiver-pytorch","url":"https://github.com/Xianchao-Wu/perceiver-pytorch"},{"title":"fac2003/perceiver-multi-modality-pytorch","url":"https://github.com/fac2003/perceiver-multi-modality-pytorch"},{"title":"sooheon/perceiver-jax","url":"https://github.com/sooheon/perceiver-jax"},{"title":"clementpoiret/Perceiver_MNIST","url":"https://github.com/clementpoiret/Perceiver_MNIST"},{"title":"yangyucheng000/University","url":"https://github.com/yangyucheng000/University/tree/main/model-1/perceiver"},{"title":"kietngt00/hmdb51-recognition","url":"https://github.com/kietngt00/hmdb51-recognition"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105976,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointGST","metrics":{"Overall Accuracy":"95.3"},"paper_url":"https://arxiv.org/abs/2410.08114v1","paper_title":"Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning","paper_date":"2024-10-10","code_links":[{"title":"jerryfeng2003/pointgst","url":"https://github.com/jerryfeng2003/pointgst"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105977,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Mamba3D + Point-MAE","metrics":{"FLOPs":"3.9G","Number of params":"16.9M","Overall Accuracy":"95.1"},"paper_url":"https://arxiv.org/abs/2404.14966v2","paper_title":"Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model","paper_date":"2024-04-23","code_links":[{"title":"xhanxu/Mamba3D","url":"https://github.com/xhanxu/Mamba3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105978,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ReCon++","metrics":{"Overall Accuracy":"95.0"},"paper_url":"https://arxiv.org/abs/2402.17766v3","paper_title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","paper_date":"2024-02-27","code_links":[{"title":"qizekun/ShapeLLM","url":"https://github.com/qizekun/ShapeLLM"},{"title":"qizekun/ReCon","url":"https://github.com/qizekun/ReCon"},{"title":"runpeidong/act","url":"https://github.com/runpeidong/act"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105979,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointGPT","metrics":{"Overall Accuracy":"94.9"},"paper_url":null,"paper_title":null,"paper_date":null,"code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105980,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"point2vec","metrics":{"Mean Accuracy":"92.0","Overall Accuracy":"94.8"},"paper_url":"https://arxiv.org/abs/2303.16570v2","paper_title":"Point2Vec for Self-Supervised Representation Learning on Point Clouds","paper_date":"2023-03-29","code_links":[{"title":"kabouzeid/point2vec","url":"https://github.com/kabouzeid/point2vec"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105981,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ULIP + PointMLP","metrics":{"Mean Accuracy":"92.4","Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105982,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ReCon","metrics":{"Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2302.02318v2","paper_title":"Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining","paper_date":"2023-02-05","code_links":[{"title":"qizekun/ReCon","url":"https://github.com/qizekun/ReCon"},{"title":"runpeidong/act","url":"https://github.com/runpeidong/act"},{"title":"aHapBean/PCP-MAE","url":"https://github.com/aHapBean/PCP-MAE"},{"title":"asterisci/point-gcc","url":"https://github.com/asterisci/point-gcc"},{"title":"qizekun/vpp","url":"https://github.com/qizekun/vpp"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105983,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"AsymDSD-B* (no voting)","metrics":{"Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2506.21724v1","paper_title":"Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning","paper_date":"2025-06-26","code_links":[{"title":"RFLeijenaar/AsymDSD","url":"https://github.com/RFLeijenaar/AsymDSD"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105984,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"RepSurf-U","metrics":{"FLOPs":"0.81G","Number of params":"1.48M","Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2205.05740v2","paper_title":"Surface Representation for Point Clouds","paper_date":"2022-05-11","code_links":[{"title":"hancyran/RepSurf","url":"https://github.com/hancyran/RepSurf"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105985,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointMLP+HyCoRe","metrics":{"Mean Accuracy":"91.9","Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2209.10318v1","paper_title":"Rethinking the compositionality of point clouds through regularization in the hyperbolic space","paper_date":"2022-09-21","code_links":[{"title":"diegovalsesia/hycore","url":"https://github.com/diegovalsesia/hycore"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105986,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointMLP","metrics":{"Mean Accuracy":"91.4","Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2202.07123v2","paper_title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","paper_date":"2022-02-15","code_links":[{"title":"ma-xu/pointmlp-pytorch","url":"https://github.com/ma-xu/pointmlp-pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105987,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-FEMAE","metrics":{"Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2312.10726v1","paper_title":"Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders","paper_date":"2023-12-17","code_links":[{"title":"zyh16143998882/aaai24-pointfemae","url":"https://github.com/zyh16143998882/aaai24-pointfemae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105988,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"OTMae3D","metrics":{"Overall Accuracy":"94.5"},"paper_url":"https://ieeexplore.ieee.org/document/10815033","paper_title":"Rethinking Masked Representation Learning for 3D Point Cloud Understanding","paper_date":"2024-12-26","code_links":[{"title":"OpenSpaceAI/OTMae3D","url":"https://github.com/OpenSpaceAI/OTMae3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105989,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointNet2+PointCMT","metrics":{"Mean Accuracy":"91.2","Number of params":"1.62M","Overall Accuracy":"94.4"},"paper_url":"https://arxiv.org/abs/2210.04208v1","paper_title":"Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis","paper_date":"2022-10-09","code_links":[{"title":"yanx27/2dpass","url":"https://github.com/yanx27/2dpass"},{"title":"zhanheshen/pointcmt","url":"https://github.com/zhanheshen/pointcmt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105990,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"IDPT","metrics":{"Overall Accuracy":"94.4"},"paper_url":"https://arxiv.org/abs/2304.07221v2","paper_title":"Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models","paper_date":"2023-04-14","code_links":[{"title":"zyh16143998882/iccv23-idpt","url":"https://github.com/zyh16143998882/iccv23-idpt"},{"title":"zyh16143998882/IDPT","url":"https://github.com/zyh16143998882/IDPT"},{"title":"zyh16143998882/aaai24-pointfemae","url":"https://github.com/zyh16143998882/aaai24-pointfemae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105991,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"OTMae3D (w/o Voting)","metrics":{"Overall Accuracy":"94.3"},"paper_url":"https://ieeexplore.ieee.org/document/10815033","paper_title":"Rethinking Masked Representation Learning for 3D Point Cloud Understanding","paper_date":"2024-12-26","code_links":[{"title":"OpenSpaceAI/OTMae3D","url":"https://github.com/OpenSpaceAI/OTMae3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105992,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"IAE + DGCNN","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2201.00785v5","paper_title":"Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning","paper_date":"2022-01-03","code_links":[{"title":"simingyan/iae","url":"https://github.com/simingyan/iae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105993,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PTv2","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2210.05666v2","paper_title":"Point Transformer V2: Grouped Vector Attention and Partition-based Pooling","paper_date":"2022-10-11","code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"Pointcept/PointTransformerV2","url":"https://github.com/Pointcept/PointTransformerV2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105994,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"CurveNet","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2105.01288v2","paper_title":"Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis","paper_date":"2021-05-04","code_links":[{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"tiangexiang/CurveNet","url":"https://github.com/tiangexiang/CurveNet"},{"title":"RF5/CurveNet","url":"https://github.com/RF5/CurveNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105995,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ExpPoint-MAE","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2306.10798v3","paper_title":"ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers","paper_date":"2023-06-19","code_links":[{"title":"vvrpanda/exppoint-mae","url":"https://github.com/vvrpanda/exppoint-mae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105996,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PCP-MAE","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2408.08753v2","paper_title":"PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders","paper_date":"2024-08-16","code_links":[{"title":"aHapBean/PCP-MAE","url":"https://github.com/aHapBean/PCP-MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105997,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"RPNet","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2108.12468v1","paper_title":"Learning Inner-Group Relations on Point Clouds","paper_date":"2021-08-27","code_links":[{"title":"hancyran/RPNet","url":"https://github.com/hancyran/RPNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":105998,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ULIP + PointBERT","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":105999,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-RAE","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2310.03670v1","paper_title":"Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning","paper_date":"2023-09-25","code_links":[{"title":"liuyyy111/point-rae","url":"https://github.com/liuyyy111/point-rae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106000,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-JEPA (voting)","metrics":{"Overall Accuracy":"94.1±0.1"},"paper_url":"https://arxiv.org/abs/2404.16432v5","paper_title":"Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud","paper_date":"2024-04-25","code_links":[{"title":"Ayumu-J-S/Point-JEPA","url":"https://github.com/Ayumu-J-S/Point-JEPA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106001,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DeLA","metrics":{"FLOPs":"1.44G","Mean Accuracy":"92.2","Number of params":"5.3M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2308.16532v1","paper_title":"Decoupled Local Aggregation for Point Cloud Learning","paper_date":"2023-08-31","code_links":[{"title":"matrix-asc/dela","url":"https://github.com/matrix-asc/dela"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106002,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"P2P","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2208.02812v2","paper_title":"P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting","paper_date":"2022-08-04","code_links":[{"title":"wangzy22/P2P","url":"https://github.com/wangzy22/P2P"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106003,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointNeXt","metrics":{"FLOPs":"6.5G","Mean Accuracy":"91.1","Number of params":"4.5M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2206.04670v2","paper_title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","paper_date":"2022-06-09","code_links":[{"title":"guochengqian/pointnext","url":"https://github.com/guochengqian/pointnext"},{"title":"linhaojia13/pointmetabase","url":"https://github.com/linhaojia13/pointmetabase"},{"title":"boyden/pointtransformerfl","url":"https://github.com/boyden/pointtransformerfl"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106004,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point Voxel Transformer","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2108.06076v4","paper_title":"PVT: Point-Voxel Transformer for Point Cloud Learning","paper_date":"2021-08-13","code_links":[{"title":"HaochengWan/PVT","url":"https://github.com/HaochengWan/PVT"},{"title":"Mind23-2/MindCode-109","url":"https://github.com/Mind23-2/MindCode-109"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106005,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-MAE","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2203.06604v2","paper_title":"Masked Autoencoders for Point Cloud Self-supervised Learning","paper_date":"2022-03-13","code_links":[{"title":"Pang-Yatian/Point-MAE","url":"https://github.com/Pang-Yatian/Point-MAE"},{"title":"lmd0311/pointmamba","url":"https://github.com/lmd0311/pointmamba"},{"title":"vvrpanda/exppoint-mae","url":"https://github.com/vvrpanda/exppoint-mae"},{"title":"liujia99/tpm","url":"https://github.com/liujia99/tpm"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106006,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-M2AE","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2205.14401v2","paper_title":"Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training","paper_date":"2022-05-28","code_links":[{"title":"zrrskywalker/i2p-mae","url":"https://github.com/zrrskywalker/i2p-mae"},{"title":"zrrskywalker/point-m2ae","url":"https://github.com/zrrskywalker/point-m2ae"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106007,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointMLS","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2401.08210v1","paper_title":"ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification","paper_date":"2024-01-16","code_links":[{"title":"fanglaosi/pointmls","url":"https://github.com/fanglaosi/pointmls"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106008,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"3D-JEPA","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2409.15803v1","paper_title":"3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning","paper_date":"2024-09-24","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106009,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"MKConv","metrics":{"Number of params":"5.63M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2107.12655v3","paper_title":"MKConv: Multidimensional Feature Representation for Point Cloud Analysis","paper_date":"2021-07-27","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106010,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"SimpleView","metrics":{"Mean Accuracy":"91.8","Overall Accuracy":"93.9"},"paper_url":"https://openreview.net/forum?id=XwATtbX3oCz","paper_title":"Revisiting Point Cloud Classification with a Simple and Effective Baseline","paper_date":"2021-01-01","code_links":[{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"IsaacCorley/simpleview-pytorch","url":"https://github.com/IsaacCorley/simpleview-pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106011,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PAConv","metrics":{"Overall Accuracy":"93.9"},"paper_url":"https://arxiv.org/abs/2103.14635v2","paper_title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","paper_date":"2021-03-26","code_links":[{"title":"PaddlePaddle/Paddle3D","url":"https://github.com/PaddlePaddle/Paddle3D"},{"title":"CVMI-Lab/PAConv","url":"https://github.com/CVMI-Lab/PAConv"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106012,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"SimpleView-DGCNN","metrics":{"Overall Accuracy":"93.9"},"paper_url":"https://arxiv.org/abs/2106.05304v1","paper_title":"Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline","paper_date":"2021-06-09","code_links":[{"title":"jiachens/ModelNet40-C","url":"https://github.com/jiachens/ModelNet40-C"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"axeber01/point-tnt","url":"https://github.com/axeber01/point-tnt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106013,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointMAE+PPT","metrics":{"Overall Accuracy":"93.88"},"paper_url":"https://arxiv.org/abs/2408.11567v1","paper_title":"Positional Prompt Tuning for Efficient 3D Representation Learning","paper_date":"2024-08-21","code_links":[{"title":"zsc000722/ppt","url":"https://github.com/zsc000722/ppt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106014,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"MVTN","metrics":{"Mean Accuracy":"92.2","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2011.13244v3","paper_title":"MVTN: Multi-View Transformation Network for 3D Shape Recognition","paper_date":"2020-11-26","code_links":[{"title":"ajhamdi/MVTN","url":"https://github.com/ajhamdi/MVTN"},{"title":"ajhamdi/mvtorch","url":"https://github.com/ajhamdi/mvtorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106015,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Feature Geometric Net (FG-Net)","metrics":{"Mean Accuracy":"91.1","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2012.09439v2","paper_title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling","paper_date":"2020-12-17","code_links":[{"title":"KangchengLiu/Feature-Geometric-Net-FG-Net","url":"https://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106016,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"GBNet","metrics":{"Mean Accuracy":"91.0","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/1911.12885v5","paper_title":"Geometric Back-projection Network for Point Cloud Classification","paper_date":"2019-11-28","code_links":[{"title":"ShiQiu0419/GFNet","url":"https://github.com/ShiQiu0419/GFNet"},{"title":"ShiQiu0419/GBNet","url":"https://github.com/ShiQiu0419/GBNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106017,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DeltaConv","metrics":{"Mean class accuracy":"91.2","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2111.08799v5","paper_title":"DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds","paper_date":"2021-11-16","code_links":[{"title":"rubenwiersma/deltaconv","url":"https://github.com/rubenwiersma/deltaconv"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106018,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"GDANet","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2012.10921v3","paper_title":"Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud","paper_date":"2020-12-20","code_links":[{"title":"mutianxu/GDANet","url":"https://github.com/mutianxu/GDANet"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106019,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-BERT","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2111.14819v2","paper_title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","paper_date":"2021-11-29","code_links":[{"title":"julie-tang00/point-bert","url":"https://github.com/julie-tang00/point-bert"},{"title":"lulutang0608/Point-BERT","url":"https://github.com/lulutang0608/Point-BERT"},{"title":"lmd0311/pointmamba","url":"https://github.com/lmd0311/pointmamba"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106020,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"APES (global-based downsample)","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2302.14673v2","paper_title":"Attention-based Point Cloud Edge Sampling","paper_date":"2023-02-28","code_links":[{"title":"JunweiZheng93/APES","url":"https://github.com/JunweiZheng93/APES"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106021,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-JEPA (no voting)","metrics":{"Overall Accuracy":"93.8±0.2"},"paper_url":"https://arxiv.org/abs/2404.16432v5","paper_title":"Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud","paper_date":"2024-04-25","code_links":[{"title":"Ayumu-J-S/Point-JEPA","url":"https://github.com/Ayumu-J-S/Point-JEPA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106022,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-PN","metrics":{"Number of params":"0.8M","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2303.08134v2","paper_title":"Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis","paper_date":"2023-03-14","code_links":[{"title":"zrrskywalker/point-nn","url":"https://github.com/zrrskywalker/point-nn"},{"title":"opengvlab/cafo","url":"https://github.com/opengvlab/cafo"},{"title":"asalarpour/Point_GN","url":"https://github.com/asalarpour/Point_GN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106023,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointSCNet","metrics":{"Mean Accuracy":"91.4","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2202.10251v2","paper_title":"PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling","paper_date":"2022-02-21","code_links":[{"title":"Chenguoz/PointSCNet","url":"https://github.com/Chenguoz/PointSCNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106024,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PRA-Net","metrics":{"Mean Accuracy":"91.2","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2112.04903v1","paper_title":"PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis","paper_date":"2021-12-09","code_links":[{"title":"xiwuchen/pra-net","url":"https://github.com/xiwuchen/pra-net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106025,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointTransformer","metrics":{"Mean Accuracy":"90.6","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2012.09164v2","paper_title":"Point Transformer","paper_date":"2020-12-16","code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"lucidrains/point-transformer-pytorch","url":"https://github.com/lucidrains/point-transformer-pytorch"},{"title":"POSTECH-CVLab/point-transformer","url":"https://github.com/POSTECH-CVLab/point-transformer"},{"title":"gofinge/pointtransformerv2","url":"https://github.com/gofinge/pointtransformerv2"},{"title":"POSTECH-CVLab/FastPointTransformer","url":"https://github.com/POSTECH-CVLab/FastPointTransformer"},{"title":"lifebeyondexpectations/eccv22-pointmixer","url":"https://github.com/lifebeyondexpectations/eccv22-pointmixer"},{"title":"engelnico/point-transformer","url":"https://github.com/engelnico/point-transformer"},{"title":"Sharpiless/Point-Transformer-Pytorch","url":"https://github.com/Sharpiless/Point-Transformer-Pytorch"},{"title":"Meowuu7/Point-Transformer","url":"https://github.com/Meowuu7/Point-Transformer"},{"title":"code-implementation1/Code6","url":"https://github.com/code-implementation1/Code6/tree/main/pointtransformer"},{"title":"jxl152/Point-Transformer","url":"https://github.com/jxl152/Point-Transformer"},{"title":"rauleun/point-transformer-tf2","url":"https://github.com/rauleun/point-transformer-tf2"},{"title":"MS-Mind/MS-Code-06","url":"https://github.com/MS-Mind/MS-Code-06/tree/main/pointtransformer"},{"title":"KernelA/pytorch-point-transformer","url":"https://github.com/KernelA/pytorch-point-transformer"},{"title":"tkdguraa/point-transformer-tensorflow","url":"https://github.com/tkdguraa/point-transformer-tensorflow"},{"title":"sh4175515/PointTransformer","url":"https://github.com/sh4175515/PointTransformer"},{"title":"alzmzyy/pointtransformer","url":"https://github.com/alzmzyy/pointtransformer"},{"title":"Mind23-2/MindCode-5","url":"https://github.com/Mind23-2/MindCode-5/tree/main/pointtransformer"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"2023-MindSpore-1/ms-code-215","url":"https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer"},{"title":"2023-MindSpore-1/ms-code-48","url":"https://github.com/2023-MindSpore-1/ms-code-48"},{"title":"MindSpore-paper-code-2/code2","url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106026,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DualMLP","metrics":{"Overall Accuracy":"93.7"},"paper_url":"https://link.springer.com/article/10.1007/s00371-023-03114-3","paper_title":"DualMLP: a two-stream fusion model for 3D point cloud classification","paper_date":"2023-10-10","code_links":[{"title":"snehaputul/DualMLP","url":"https://github.com/snehaputul/DualMLP"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106027,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointMixer","metrics":{"Mean Accuracy":"91.4","Number of params":"6.5M","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2111.11187v5","paper_title":"PointMixer: MLP-Mixer for Point Cloud Understanding","paper_date":"2021-11-22","code_links":[{"title":"lifebeyondexpectations/eccv22-pointmixer","url":"https://github.com/lifebeyondexpectations/eccv22-pointmixer"},{"title":"LifeBeyondExpectations/PointMixer","url":"https://github.com/LifeBeyondExpectations/PointMixer"},{"title":"hrisi/tree-species-classification","url":"https://github.com/hrisi/tree-species-classification"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106028,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DeepGCN","metrics":{"Mean Accuracy":"90.9","Number of params":"2.2M","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/1910.06849v3","paper_title":"DeepGCNs: Making GCNs Go as Deep as CNNs","paper_date":"2019-10-15","code_links":[{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb"},{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_arxiv_dgl"},{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch"},{"title":"lightaime/deep_gcns","url":"https://github.com/lightaime/deep_gcns"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106029,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"LCPFormer","metrics":{"Mean Accuracy":"90.7","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2210.12755v2","paper_title":"LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers","paper_date":"2022-10-23","code_links":[{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106030,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DGCNN + SageMix","metrics":{"Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106031,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointVector-S","metrics":{"Mean Accuracy":"91","Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2205.10528v3","paper_title":"PointVector: A Vector Representation In Point Cloud Analysis","paper_date":"2022-05-21","code_links":[{"title":"guochengqian/openpoints","url":"https://github.com/guochengqian/openpoints/blob/master/models/backbone/pointvector.py"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106032,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"RSMix","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2102.01929v3","paper_title":"Regularization Strategy for Point Cloud via Rigidly Mixed Sample","paper_date":"2021-02-03","code_links":[{"title":"dogyoonlee/RSMix-official","url":"https://github.com/dogyoonlee/RSMix-official"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106033,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointConT","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2303.04599v1","paper_title":"Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space","paper_date":"2023-03-08","code_links":[{"title":"yahuiliu99/pointcont","url":"https://github.com/yahuiliu99/pointcont"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106034,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"APES (local-based downsample)","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2302.14673v2","paper_title":"Attention-based Point Cloud Edge Sampling","paper_date":"2023-02-28","code_links":[{"title":"JunweiZheng93/APES","url":"https://github.com/JunweiZheng93/APES"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106035,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DSPoint","metrics":{"Number of params":"1.16M","Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2111.10332v4","paper_title":"DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion","paper_date":"2021-11-19","code_links":[{"title":"adonis-galaxy/dspoint","url":"https://github.com/adonis-galaxy/dspoint"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106036,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ULIP + PointNet++(ssg)","metrics":{"Mean Accuracy":"91.2","Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106037,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointCutmix","metrics":{"Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2101.01461v2","paper_title":"PointCutMix: Regularization Strategy for Point Cloud Classification","paper_date":"2021-01-05","code_links":[{"title":"jiachens/ModelNet40-C","url":"https://github.com/jiachens/ModelNet40-C"},{"title":"cuge1995/PointCutMix","url":"https://github.com/cuge1995/PointCutMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106038,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"3DMedPT","metrics":{"Number of params":"1.54M","Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2112.04863v2","paper_title":"3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis","paper_date":"2021-12-09","code_links":[{"title":"crane-papercode/3dmedpt","url":"https://github.com/crane-papercode/3dmedpt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106039,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DGCNN + MD","metrics":{"Mean Accuracy":"89.88","Overall Accuracy":"93.39"},"paper_url":"https://arxiv.org/abs/2109.00452v3","paper_title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","paper_date":"2021-09-01","code_links":[{"title":"cyysc1998/3D-Pretraining","url":"https://github.com/cyysc1998/3D-Pretraining"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106040,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"OGNet + MD","metrics":{"Mean Accuracy":"90.71","Overall Accuracy":"93.31"},"paper_url":"https://arxiv.org/abs/2109.00452v3","paper_title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","paper_date":"2021-09-01","code_links":[{"title":"cyysc1998/3D-Pretraining","url":"https://github.com/cyysc1998/3D-Pretraining"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106041,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"OG-Net-Small","metrics":{"Mean Accuracy":"90.5","Number of params":"1.22M","Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2006.04569v3","paper_title":"Parameter-Efficient Person Re-identification in the 3D Space","paper_date":"2020-06-08","code_links":[{"title":"layumi/person-reid-3d","url":"https://github.com/layumi/person-reid-3d"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106042,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointStack","metrics":{"Mean Accuracy":"89.6","Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2205.09962v1","paper_title":"Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling","paper_date":"2022-05-20","code_links":[{"title":"LongerVision/PointStack","url":"https://github.com/LongerVision/PointStack"},{"title":"kaist-avelab/PointStack","url":"https://github.com/kaist-avelab/PointStack"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106043,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointNet++ + SageMix","metrics":{"Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106044,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointASNL","metrics":{"Overall Accuracy":"93.2"},"paper_url":"https://arxiv.org/abs/2003.00492v3","paper_title":"PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling","paper_date":"2020-03-01","code_links":[{"title":"yanx27/PointASNL","url":"https://github.com/yanx27/PointASNL"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106045,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point Cloud Transformer","metrics":{"Number of params":"2.88M","Overall Accuracy":"93.2"},"paper_url":"https://arxiv.org/abs/2012.09688v4","paper_title":"PCT: Point cloud transformer","paper_date":"2020-12-17","code_links":[{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"MenghaoGuo/PCT","url":"https://github.com/MenghaoGuo/PCT"},{"title":"Strawberry-Eat-Mango/PCT_Pytorch","url":"https://github.com/Strawberry-Eat-Mango/PCT_Pytorch"},{"title":"uyzhang/PCT_Pytorch","url":"https://github.com/uyzhang/PCT_Pytorch"},{"title":"uuyzhang/PCT_Pytorch","url":"https://github.com/uuyzhang/PCT_Pytorch"},{"title":"irmvlab/point-mamba","url":"https://github.com/irmvlab/point-mamba"},{"title":"qinglew/PointCloudTransformer","url":"https://github.com/qinglew/PointCloudTransformer"},{"title":"Liu-Feng/PCT-tensorflow","url":"https://github.com/Liu-Feng/PCT-tensorflow"},{"title":"ViniciusMikuni/PCT_HEP","url":"https://github.com/ViniciusMikuni/PCT_HEP"},{"title":"toothfairy42/PCT-pytorch","url":"https://github.com/toothfairy42/PCT-pytorch"},{"title":"DLW3D/PCT_Pytorch","url":"https://github.com/DLW3D/PCT_Pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106046,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DRNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/2005.06734v2","paper_title":"Dense-Resolution Network for Point Cloud Classification and Segmentation","paper_date":"2020-05-14","code_links":[{"title":"ShiQiu0419/DRNet","url":"https://github.com/ShiQiu0419/DRNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106047,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ShellNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/1908.06295v1","paper_title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","paper_date":"2019-08-17","code_links":[{"title":"hkust-vgd/shellnet","url":"https://github.com/hkust-vgd/shellnet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106048,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"STRL + DGCNN","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/2109.00179v1","paper_title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","paper_date":"2021-09-01","code_links":[{"title":"yichen928/STRL","url":"https://github.com/yichen928/STRL"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106049,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PatchAugment+DGCNN","metrics":{"Overall Accuracy":"93.1"},"paper_url":null,"paper_title":null,"paper_date":null,"code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106050,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"AdaCrossNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://inass.org/wp-content/uploads/2024/10/2025022911-2.pdf","paper_title":"AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning","paper_date":"2025-01-02","code_links":[{"title":"virgantara/AdaCrossNet","url":"https://github.com/virgantara/AdaCrossNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106051,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointManifold","metrics":{"Mean Accuracy":"90.4","Overall Accuracy":"93.0"},"paper_url":"https://arxiv.org/abs/2010.07215v2","paper_title":"PointManifold: Using Manifold Learning for Point Cloud Classification","paper_date":"2020-10-14","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106052,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"InterpCNN","metrics":{"Overall Accuracy":"93.0"},"paper_url":"https://arxiv.org/abs/1908.04512v1","paper_title":"Interpolated Convolutional Networks for 3D Point Cloud Understanding","paper_date":"2019-08-13","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106053,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DGCNN","metrics":{"Mean Accuracy":"90.2","Number of params":"1.81M","Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1801.07829v2","paper_title":"Dynamic Graph CNN for Learning on Point Clouds","paper_date":"2018-01-24","code_links":[{"title":"WangYueFt/dgcnn","url":"https://github.com/WangYueFt/dgcnn"},{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"AnTao97/dgcnn.pytorch","url":"https://github.com/AnTao97/dgcnn.pytorch"},{"title":"bupt-gamma/gammagl","url":"https://github.com/bupt-gamma/gammagl"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"hqucms/ParticleNet","url":"https://github.com/hqucms/ParticleNet"},{"title":"lingzhang1/ContrastNet","url":"https://github.com/lingzhang1/ContrastNet"},{"title":"cy-xu/spatially_aware_ai","url":"https://github.com/cy-xu/spatially_aware_ai"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"},{"title":"hansen7/NRS_3D","url":"https://github.com/hansen7/NRS_3D"},{"title":"brent-murray/tr3d_pointaugdgcnn","url":"https://github.com/brent-murray/tr3d_pointaugdgcnn"},{"title":"nnn911/MLSI","url":"https://github.com/nnn911/MLSI"},{"title":"lingzhang1/dgcnn_sampling","url":"https://github.com/lingzhang1/dgcnn_sampling"},{"title":"lingzhang1/dgcnn_v2","url":"https://github.com/lingzhang1/dgcnn_v2"},{"title":"xrndai/dgcnn_amino_acid_structure","url":"https://github.com/xrndai/dgcnn_amino_acid_structure"},{"title":"JingfeiHuang/DGCNN-Paddle","url":"https://github.com/JingfeiHuang/DGCNN-Paddle"},{"title":"af13s/dgcnn-amino","url":"https://github.com/af13s/dgcnn-amino"},{"title":"Squanch-U/DGCNN","url":"https://github.com/Squanch-U/DGCNN"},{"title":"AmitBracha/GIP_project","url":"https://github.com/AmitBracha/GIP_project"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106054,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"KPConv","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1904.08889v2","paper_title":"KPConv: Flexible and Deformable Convolution for Point Clouds","paper_date":"2019-04-18","code_links":[{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"HuguesTHOMAS/KPConv-PyTorch","url":"https://github.com/HuguesTHOMAS/KPConv-PyTorch"},{"title":"HuguesTHOMAS/KPConv","url":"https://github.com/HuguesTHOMAS/KPConv"},{"title":"ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D"},{"title":"XuyangBai/KPConv.pytorch","url":"https://github.com/XuyangBai/KPConv.pytorch"},{"title":"plusmultiply/mprm","url":"https://github.com/plusmultiply/mprm"},{"title":"Arjun-NA/KPConv_for_DALES","url":"https://github.com/Arjun-NA/KPConv_for_DALES"},{"title":"Yacovitch/EyeNet","url":"https://github.com/Yacovitch/EyeNet"},{"title":"genglinliu/KPConv_Pytorch","url":"https://github.com/genglinliu/KPConv_Pytorch"},{"title":"JohnRomanelis/KPConv_torch_geometric","url":"https://github.com/JohnRomanelis/KPConv_torch_geometric"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106055,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"RS-CNN","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1904.07601v3","paper_title":"Relation-Shape Convolutional Neural Network for Point Cloud Analysis","paper_date":"2019-04-16","code_links":[{"title":"Yochengliu/Relation-Shape-CNN","url":"https://github.com/Yochengliu/Relation-Shape-CNN"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"sausagecy/RSCNN_Pytorch1.0","url":"https://github.com/sausagecy/RSCNN_Pytorch1.0"},{"title":"panyunyi97/RSCNN","url":"https://github.com/panyunyi97/RSCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106056,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ASSANet","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/2110.10538v2","paper_title":"ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning","paper_date":"2021-10-20","code_links":[{"title":"guochengqian/assanet","url":"https://github.com/guochengqian/assanet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106057,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-M2AE-SVM","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/2205.14401v2","paper_title":"Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training","paper_date":"2022-05-28","code_links":[{"title":"zrrskywalker/i2p-mae","url":"https://github.com/zrrskywalker/i2p-mae"},{"title":"zrrskywalker/point-m2ae","url":"https://github.com/zrrskywalker/point-m2ae"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106058,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point Transformer","metrics":{"Overall Accuracy":"92.8"},"paper_url":"https://arxiv.org/abs/2011.00931v2","paper_title":"Point Transformer","paper_date":"2020-11-02","code_links":[{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"engelnico/point-transformer","url":"https://github.com/engelnico/point-transformer"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106059,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"A-CNN","metrics":{"Overall Accuracy":"92.6"},"paper_url":"http://arxiv.org/abs/1904.08017v1","paper_title":"A-CNN: Annularly Convolutional Neural Networks on Point Clouds","paper_date":"2019-04-16","code_links":[{"title":"artemkomarichev/a-cnn","url":"https://github.com/artemkomarichev/a-cnn"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106060,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"P2Sequence","metrics":{"Overall Accuracy":"92.6"},"paper_url":"http://arxiv.org/abs/1811.02565v2","paper_title":"Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network","paper_date":"2018-11-06","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106061,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-TnT","metrics":{"Number of params":"3.9M","Overall Accuracy":"92.6"},"paper_url":"https://arxiv.org/abs/2204.03957v1","paper_title":"Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition","paper_date":"2022-04-08","code_links":[{"title":"axeber01/point-tnt","url":"https://github.com/axeber01/point-tnt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106062,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointConv","metrics":{"Overall Accuracy":"92.5"},"paper_url":"https://arxiv.org/abs/1811.07246v3","paper_title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","paper_date":"2018-11-17","code_links":[{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"DylanWusee/pointconv","url":"https://github.com/DylanWusee/pointconv"},{"title":"DylanWusee/pointconv_pytorch","url":"https://github.com/DylanWusee/pointconv_pytorch"},{"title":"dgriffiths3/pointconv-tensorflow2","url":"https://github.com/dgriffiths3/pointconv-tensorflow2"},{"title":"Young98CN/pointconv_pytorch","url":"https://github.com/Young98CN/pointconv_pytorch"},{"title":"Ghailen-Ben-Achour/PointConv_segmentation","url":"https://github.com/Ghailen-Ben-Achour/PointConv_segmentation"},{"title":"koritsky/pointconv","url":"https://github.com/koritsky/pointconv"},{"title":"THHHomas/mls","url":"https://github.com/THHHomas/mls"},{"title":"ECE685-FinalProject/3D-object-recognition","url":"https://github.com/ECE685-FinalProject/3D-object-recognition"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106063,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PolyNet","metrics":{"Overall Accuracy":"92.42"},"paper_url":"https://arxiv.org/abs/2110.07882v1","paper_title":"PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation","paper_date":"2021-10-15","code_links":[{"title":"myavartanoo/PolyNet_PyTorch","url":"https://github.com/myavartanoo/PolyNet_PyTorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106064,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"SpiderCNN","metrics":{"Overall Accuracy":"92.4"},"paper_url":"http://arxiv.org/abs/1803.11527v3","paper_title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","paper_date":"2018-03-30","code_links":[{"title":"xyf513/SpiderCNN","url":"https://github.com/xyf513/SpiderCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106065,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PCNN","metrics":{"Overall Accuracy":"92.3"},"paper_url":"http://arxiv.org/abs/1803.10091v1","paper_title":"Point Convolutional Neural Networks by Extension Operators","paper_date":"2018-03-27","code_links":[{"title":"matanatz/pcnn","url":"https://github.com/matanatz/pcnn"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106066,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointCNN","metrics":{"Overall Accuracy":"92.2"},"paper_url":"http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points","paper_title":"PointCNN: Convolution On X-Transformed Points","paper_date":"2018-12-01","code_links":[{"title":"yangyanli/PointCNN","url":"https://github.com/yangyanli/PointCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106067,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Point-PlaneNet","metrics":{"Mean Accuracy":"90.5","Overall Accuracy":"92.1"},"paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S1051200419301873","paper_title":"Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis","paper_date":"2020-05-01","code_links":[{"title":"moeinp70/Point-Planenet","url":"https://github.com/moeinp70/Point-Planenet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106068,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"SpecGCN","metrics":{"Overall Accuracy":"92.1"},"paper_url":"http://arxiv.org/abs/1803.05827v1","paper_title":"Local Spectral Graph Convolution for Point Set Feature Learning","paper_date":"2018-03-15","code_links":[{"title":"fate3439/LocalSpecGCN","url":"https://github.com/fate3439/LocalSpecGCN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106069,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"DynamicScale","metrics":{"Overall Accuracy":"92.1"},"paper_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9565556","paper_title":"Dynamic Local Geometry Capture in 3D PointCloud Classification","paper_date":"2021-10-19","code_links":[{"title":"VimsLab/DynamicScale","url":"https://github.com/VimsLab/DynamicScale"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106070,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointGrid","metrics":{"Overall Accuracy":"92.0"},"paper_url":"http://openaccess.thecvf.com/content_cvpr_2018/html/Le_PointGrid_A_Deep_CVPR_2018_paper.html","paper_title":"PointGrid: A Deep Network for 3D Shape Understanding","paper_date":"2018-06-01","code_links":[{"title":"trucleduc/PointGrid","url":"https://github.com/trucleduc/PointGrid"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106071,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Kd-Net","metrics":{"Overall Accuracy":"91.8"},"paper_url":"http://arxiv.org/abs/1704.01222v2","paper_title":"Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models","paper_date":"2017-04-04","code_links":[{"title":"fxia22/kdnet.pytorch","url":"https://github.com/fxia22/kdnet.pytorch"},{"title":"qq456cvb/KdNet-Tensorflow","url":"https://github.com/qq456cvb/KdNet-Tensorflow"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106072,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Overall Accuracy":"91.7"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/8354322","paper_title":"General-Purpose Deep Point Cloud Feature Extractor","paper_date":"2018-03-12","code_links":[{"title":"WDot/G3DNet","url":"https://github.com/WDot/G3DNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106073,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"3DMFV-Net","metrics":{"Overall Accuracy":"91.6"},"paper_url":"http://arxiv.org/abs/1711.08241v1","paper_title":"3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks","paper_date":"2017-11-22","code_links":[{"title":"sitzikbs/3DmFV-Net","url":"https://github.com/sitzikbs/3DmFV-Net"},{"title":"sitzikbs/3DmFV-Net-MATLAB","url":"https://github.com/sitzikbs/3DmFV-Net-MATLAB"},{"title":"fferroni/fisher_vector_classifier","url":"https://github.com/fferroni/fisher_vector_classifier"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106074,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Overall Accuracy":"91.49"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/10229841","paper_title":"CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud","paper_date":"2023-06-08","code_links":[{"title":"snehaputul/CrossMoCo","url":"https://github.com/snehaputul/CrossMoCo"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106075,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"SO-Net","metrics":{"Overall Accuracy":"90.9"},"paper_url":"http://arxiv.org/abs/1803.04249v4","paper_title":"SO-Net: Self-Organizing Network for Point Cloud Analysis","paper_date":"2018-03-12","code_links":[{"title":"lijx10/SO-Net","url":"https://github.com/lijx10/SO-Net"},{"title":"donnyruixu/pc-elm-ae","url":"https://github.com/donnyruixu/pc-elm-ae"},{"title":"LONG-9621/SO-Net","url":"https://github.com/LONG-9621/SO-Net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106076,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointNet++","metrics":{"Number of params":"1.74M","Overall Accuracy":"90.7"},"paper_url":"http://arxiv.org/abs/1706.02413v1","paper_title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","paper_date":"2017-06-07","code_links":[{"title":"yanx27/Pointnet_Pointnet2_pytorch","url":"https://github.com/yanx27/Pointnet_Pointnet2_pytorch"},{"title":"charlesq34/pointnet2","url":"https://github.com/charlesq34/pointnet2"},{"title":"facebookresearch/votenet","url":"https://github.com/facebookresearch/votenet"},{"title":"erikwijmans/Pointnet2_PyTorch","url":"https://github.com/erikwijmans/Pointnet2_PyTorch"},{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"sshaoshuai/Pointnet2.PyTorch","url":"https://github.com/sshaoshuai/Pointnet2.PyTorch"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/pointnet2"},{"title":"zaiweizhang/H3DNet","url":"https://github.com/zaiweizhang/H3DNet"},{"title":"hehefan/P4Transformer","url":"https://github.com/hehefan/P4Transformer"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"thu17cyz/3DIoUMatch","url":"https://github.com/thu17cyz/3DIoUMatch"},{"title":"hehefan/PointRNN","url":"https://github.com/hehefan/PointRNN"},{"title":"facebookresearch/imvotenet","url":"https://github.com/facebookresearch/imvotenet"},{"title":"referit3d/referit3d","url":"https://github.com/referit3d/referit3d"},{"title":"hehefan/Point-Spatio-Temporal-Convolution","url":"https://github.com/hehefan/Point-Spatio-Temporal-Convolution"},{"title":"dgriffiths3/pointnet2-tensorflow2","url":"https://github.com/dgriffiths3/pointnet2-tensorflow2"},{"title":"nickgkan/beauty_detr","url":"https://github.com/nickgkan/beauty_detr"},{"title":"nickgkan/butd_detr","url":"https://github.com/nickgkan/butd_detr"},{"title":"open-air-sun/pq-transformer","url":"https://github.com/open-air-sun/pq-transformer"},{"title":"hehefan/PointRNN-PyTorch","url":"https://github.com/hehefan/PointRNN-PyTorch"},{"title":"joyhsu0504/ns3d","url":"https://github.com/joyhsu0504/ns3d"},{"title":"curryyuan/x-trans2cap","url":"https://github.com/curryyuan/x-trans2cap"},{"title":"zyang-ur/SAT","url":"https://github.com/zyang-ur/SAT"},{"title":"iballester/SPiKE","url":"https://github.com/iballester/SPiKE"},{"title":"johnsonsign/mast-pre","url":"https://github.com/johnsonsign/mast-pre"},{"title":"AsahiLiu/PointDetectron","url":"https://github.com/AsahiLiu/PointDetectron"},{"title":"hehefan/PSTNet2","url":"https://github.com/hehefan/PSTNet2"},{"title":"JohnsonSign/PointCMP","url":"https://github.com/JohnsonSign/PointCMP"},{"title":"dfki-av/mikasa-3dvg","url":"https://github.com/dfki-av/mikasa-3dvg"},{"title":"Ghailen-Ben-Achour/PointNet2_Segmentation","url":"https://github.com/Ghailen-Ben-Achour/PointNet2_Segmentation"},{"title":"houseleo/pointnet","url":"https://github.com/houseleo/pointnet"},{"title":"tao-tao-tao-tao-tao/diffusion_suction","url":"https://github.com/tao-tao-tao-tao-tao/diffusion_suction"},{"title":"Lonepic/SPIB","url":"https://github.com/Lonepic/SPIB"},{"title":"asalarpour/Point_GN","url":"https://github.com/asalarpour/Point_GN"},{"title":"LinZhuoChen/pointnet2_multi_gpu","url":"https://github.com/LinZhuoChen/pointnet2_multi_gpu"},{"title":"shaygu62/Protein-Classification","url":"https://github.com/shaygu62/Protein-Classification"},{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"},{"title":"ikh-innovation/roboweldar-votenet","url":"https://github.com/ikh-innovation/roboweldar-votenet"},{"title":"witignite/Frustum-PointNet","url":"https://github.com/witignite/Frustum-PointNet"},{"title":"airalcorn2/parking-lot-pointnetplusplus","url":"https://github.com/airalcorn2/parking-lot-pointnetplusplus"},{"title":"ftdlyc/pointnet_pytorch","url":"https://github.com/ftdlyc/pointnet_pytorch"},{"title":"tingxueronghua/dpke","url":"https://github.com/tingxueronghua/dpke"},{"title":"timothylimyl/PointNet-Pytorch","url":"https://github.com/timothylimyl/PointNet-Pytorch"},{"title":"zhh6425/MotionPointNet","url":"https://github.com/zhh6425/MotionPointNet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/ascend_pointnet2"},{"title":"hoangcuongbk80/VoteGrasp","url":"https://github.com/hoangcuongbk80/VoteGrasp"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet2"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/PointNet2"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/PointNet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/gpu_pointnet2"},{"title":"tonysy/pointnet2_tf","url":"https://github.com/tonysy/pointnet2_tf"},{"title":"Xiangxu-0103/Octant-CNN","url":"https://github.com/Xiangxu-0103/Octant-CNN"},{"title":"Lw510107/PointNet","url":"https://github.com/Lw510107/PointNet"},{"title":"xurui1217/pointnet2-master","url":"https://github.com/xurui1217/pointnet2-master"},{"title":"SY-Xuan/pointnet_plus_plus_paddlepaddle","url":"https://github.com/SY-Xuan/pointnet_plus_plus_paddlepaddle"},{"title":"LONG-9621/VoteNet","url":"https://github.com/LONG-9621/VoteNet"},{"title":"LONG-9621/PointNet-","url":"https://github.com/LONG-9621/PointNet-"},{"title":"Harut0726/votenet","url":"https://github.com/Harut0726/votenet"},{"title":"FlowWind1999/pointnet-2","url":"https://github.com/FlowWind1999/pointnet-2"},{"title":"llzlcl/pointcloud-segment","url":"https://github.com/llzlcl/pointcloud-segment"},{"title":"zenroad/modifypointnet","url":"https://github.com/zenroad/modifypointnet"},{"title":"caizeyu1992/pointnet2","url":"https://github.com/caizeyu1992/pointnet2"},{"title":"2023-MindSpore-1/ms-code-8","url":"https://github.com/2023-MindSpore-1/ms-code-8/tree/main/PointNet2"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"2023-MindSpore-1/ms-code-223","url":"https://github.com/2023-MindSpore-1/ms-code-223/tree/main/PointNet"},{"title":"MindSpore-paper-code-3/code4","url":"https://github.com/MindSpore-paper-code-3/code4/tree/main/frustum-pointnet"},{"title":"2023-MindSpore-1/ms-code-223","url":"https://github.com/2023-MindSpore-1/ms-code-223/tree/main/PointNet2"},{"title":"brbzjl/pointnet2","url":"https://github.com/brbzjl/pointnet2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106077,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Kd-net","metrics":{"Overall Accuracy":"90.6"},"paper_url":"http://arxiv.org/abs/1704.01222v2","paper_title":"Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models","paper_date":"2017-04-04","code_links":[{"title":"fxia22/kdnet.pytorch","url":"https://github.com/fxia22/kdnet.pytorch"},{"title":"qq456cvb/KdNet-Tensorflow","url":"https://github.com/qq456cvb/KdNet-Tensorflow"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106078,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointNet + SageMix","metrics":{"Overall Accuracy":"90.3"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106079,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"MVCNN","metrics":{"Overall Accuracy":"90.1"},"paper_url":"http://arxiv.org/abs/1505.00880v3","paper_title":"Multi-view Convolutional Neural Networks for 3D Shape Recognition","paper_date":"2015-05-05","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106080,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"PointNet","metrics":{"Mean Accuracy":"86.0","Number of params":"3.47M","Overall Accuracy":"89.2"},"paper_url":"http://arxiv.org/abs/1612.00593v2","paper_title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","paper_date":"2016-12-02","code_links":[{"title":"charlesq34/pointnet","url":"https://github.com/charlesq34/pointnet"},{"title":"fxia22/pointnet.pytorch","url":"https://github.com/fxia22/pointnet.pytorch"},{"title":"WangYueFt/dgcnn","url":"https://github.com/WangYueFt/dgcnn"},{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"DylanWusee/pointconv_pytorch","url":"https://github.com/DylanWusee/pointconv_pytorch"},{"title":"nikitakaraevv/pointnet","url":"https://github.com/nikitakaraevv/pointnet"},{"title":"maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification","url":"https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"sarthakTUM/roofn3d","url":"https://github.com/sarthakTUM/roofn3d"},{"title":"jtpils/treegan","url":"https://github.com/jtpils/treegan"},{"title":"ajhamdi/AdvPC","url":"https://github.com/ajhamdi/AdvPC"},{"title":"soumik12345/point-cloud-segmentation","url":"https://github.com/soumik12345/point-cloud-segmentation"},{"title":"AI-Guru/pointcloud_experiments","url":"https://github.com/AI-Guru/pointcloud_experiments"},{"title":"ali-stanford/pointnetcfd","url":"https://github.com/ali-stanford/pointnetcfd"},{"title":"romaintha/pytorch_pointnet","url":"https://github.com/romaintha/pytorch_pointnet"},{"title":"saaries/PointNet","url":"https://github.com/saaries/PointNet"},{"title":"Fragjacker/Pointcloud-grad-CAM","url":"https://github.com/Fragjacker/Pointcloud-grad-CAM"},{"title":"ZhihaoZhu/PointNet-Implementation-Tensorflow","url":"https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow"},{"title":"YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti","url":"https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti"},{"title":"Yuto0107/pointnet","url":"https://github.com/Yuto0107/pointnet"},{"title":"YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024","url":"https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024"},{"title":"opeco17/pointnet","url":"https://github.com/opeco17/pointnet"},{"title":"Dir-b/PointNet","url":"https://github.com/Dir-b/PointNet"},{"title":"Young98CN/pointconv_pytorch","url":"https://github.com/Young98CN/pointconv_pytorch"},{"title":"y2kmz/pointnetv2","url":"https://github.com/y2kmz/pointnetv2"},{"title":"YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization","url":"https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization"},{"title":"imanlab/deep_movement_primitives","url":"https://github.com/imanlab/deep_movement_primitives"},{"title":"abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds","url":"https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds"},{"title":"donshen/pointnet.phasedetection","url":"https://github.com/donshen/pointnet.phasedetection"},{"title":"ModelBunker/PointNet-TensorFlow","url":"https://github.com/ModelBunker/PointNet-TensorFlow"},{"title":"amyllykoski/CycleGAN","url":"https://github.com/amyllykoski/CycleGAN"},{"title":"Javed002/Object_detection_PointNet","url":"https://github.com/Javed002/Object_detection_PointNet"},{"title":"sanantoniochili/PointCloud_KNN","url":"https://github.com/sanantoniochili/PointCloud_KNN"},{"title":"PaParaZz1/PointNet","url":"https://github.com/PaParaZz1/PointNet"},{"title":"yevgm/deepadv3d","url":"https://github.com/yevgm/deepadv3d"},{"title":"witignite/Frustum-PointNet","url":"https://github.com/witignite/Frustum-PointNet"},{"title":"m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks","url":"https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks"},{"title":"alpemek/ais3d","url":"https://github.com/alpemek/ais3d"},{"title":"yanxp/PointNet","url":"https://github.com/yanxp/PointNet"},{"title":"ftdlyc/pointnet_pytorch","url":"https://github.com/ftdlyc/pointnet_pytorch"},{"title":"Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification","url":"https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification"},{"title":"Phimos/Paddle-PointNet","url":"https://github.com/Phimos/Paddle-PointNet"},{"title":"zgx0534/pointnet_win","url":"https://github.com/zgx0534/pointnet_win"},{"title":"timothylimyl/PointNet-Pytorch","url":"https://github.com/timothylimyl/PointNet-Pytorch"},{"title":"minhncedutw/pointnet1_keras","url":"https://github.com/minhncedutw/pointnet1_keras"},{"title":"Jagyan/ghost_radarpoint_detection","url":"https://github.com/Jagyan/ghost_radarpoint_detection"},{"title":"brendanmallery9/entropic-barycenters","url":"https://github.com/brendanmallery9/entropic-barycenters"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet"},{"title":"xiuyu0000/vision","url":"https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py"},{"title":"hnmspirit/pointnet1","url":"https://github.com/hnmspirit/pointnet1"},{"title":"yangyucheng000/pointnet","url":"https://github.com/yangyucheng000/pointnet"},{"title":"eth212/Qmind_ModelNet10_PointNet","url":"https://github.com/eth212/Qmind_ModelNet10_PointNet"},{"title":"smfirmin/DeepLearningFinalProject","url":"https://github.com/smfirmin/DeepLearningFinalProject"},{"title":"ECE685-FinalProject/3D-object-recognition","url":"https://github.com/ECE685-FinalProject/3D-object-recognition"},{"title":"KhusDM/PointNetTree","url":"https://github.com/KhusDM/PointNetTree"},{"title":"Lw510107/pointnet-2018.6.27-","url":"https://github.com/Lw510107/pointnet-2018.6.27-"},{"title":"xurui1217/pointnet.pytorch-master","url":"https://github.com/xurui1217/pointnet.pytorch-master"},{"title":"hnVfly/pointnet.mxnet","url":"https://github.com/hnVfly/pointnet.mxnet"},{"title":"LebronGG/PointNet","url":"https://github.com/LebronGG/PointNet"},{"title":"GOD-GOD-Autonomous-Vehicle/self-pointnet","url":"https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet"},{"title":"AlfredoZermini/PointNet","url":"https://github.com/AlfredoZermini/PointNet"},{"title":"wonderland-dsg/pointnet-grid","url":"https://github.com/wonderland-dsg/pointnet-grid"},{"title":"aviros/roatationPointnet","url":"https://github.com/aviros/roatationPointnet"},{"title":"THHHomas/mls","url":"https://github.com/THHHomas/mls"},{"title":"liuch37/pointnet","url":"https://github.com/liuch37/pointnet"},{"title":"yanx27/Pointnet","url":"https://github.com/yanx27/Pointnet"},{"title":"Taeuk-Jang/pointcompletion","url":"https://github.com/Taeuk-Jang/pointcompletion"},{"title":"ytng001/sensemaking","url":"https://github.com/ytng001/sensemaking"},{"title":"nddoshi/pointnet","url":"https://github.com/nddoshi/pointnet"},{"title":"SY-Xuan/pointnet_paddle","url":"https://github.com/SY-Xuan/pointnet_paddle"},{"title":"Cmeijiang/PointNet_MS","url":"https://github.com/Cmeijiang/PointNet_MS"},{"title":"AndreiMoraru123/PointNet","url":"https://github.com/AndreiMoraru123/PointNet"},{"title":"mengxingshifen1218/pointnet.pytorch","url":"https://github.com/mengxingshifen1218/pointnet.pytorch"},{"title":"Moon-Xuezha/PointNet","url":"https://github.com/Moon-Xuezha/PointNet"},{"title":"ajertec/PointNetKeras","url":"https://github.com/ajertec/PointNetKeras"},{"title":"2023-MindSpore-1/ms-code-35","url":"https://github.com/2023-MindSpore-1/ms-code-35"},{"title":"LONG-9621/Extract_Point_3D","url":"https://github.com/LONG-9621/Extract_Point_3D"},{"title":"Mind23-2/MindCode-63","url":"https://github.com/Mind23-2/MindCode-63"},{"title":"merazlab/3D_Deep_Learning_Link","url":"https://github.com/merazlab/3D_Deep_Learning_Link"},{"title":"2023-MindSpore-1/ms-code-7","url":"https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet"},{"title":"KaidongLi/tf-3d-alpha","url":"https://github.com/KaidongLi/tf-3d-alpha"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"prasadsawant5/PointNet","url":"https://github.com/prasadsawant5/PointNet"},{"title":"SonuDileep/3-D-Object-Detection-using-PointNet","url":"https://github.com/SonuDileep/3-D-Object-Detection-using-PointNet"},{"title":"BPMJG/annotated_pointnet","url":"https://github.com/BPMJG/annotated_pointnet"},{"title":"debajyotid2/pointnet_pl","url":"https://github.com/debajyotid2/pointnet_pl"},{"title":"SBPL-Cruz/perch_pose_sampler","url":"https://github.com/SBPL-Cruz/perch_pose_sampler"},{"title":"2023-MindSpore-4/Code9","url":"https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet"},{"title":"wuryantoAji/POINTNET","url":"https://github.com/wuryantoAji/POINTNET"},{"title":"LONG-9621/PointNet","url":"https://github.com/LONG-9621/PointNet"},{"title":"bt77/pointnet","url":"https://github.com/bt77/pointnet"},{"title":"MindSpore-paper-code-3/code9","url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2"},{"title":"KiranAkadas/My_Pointnet_v2","url":"https://github.com/KiranAkadas/My_Pointnet_v2"},{"title":"YiruS/pointnet_adversarial","url":"https://github.com/YiruS/pointnet_adversarial"},{"title":"Harut0726/my-pointnet-tensorflow","url":"https://github.com/Harut0726/my-pointnet-tensorflow"},{"title":"AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation","url":"https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation"},{"title":"hz-ants/pointnet.pytorch","url":"https://github.com/hz-ants/pointnet.pytorch"},{"title":"VaanHUANG/CSCI5210HW1","url":"https://github.com/VaanHUANG/CSCI5210HW1"},{"title":"MindSpore-paper-code-3/code9","url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet"},{"title":"ShadowShadowWong/update-pointnet-work","url":"https://github.com/ShadowShadowWong/update-pointnet-work"},{"title":"Yang2446/pointnet","url":"https://github.com/Yang2446/pointnet"},{"title":"lingzhang1/pointnet_pytorch","url":"https://github.com/lingzhang1/pointnet_pytorch"},{"title":"dwtstore/sfm1","url":"https://github.com/dwtstore/sfm1"},{"title":"freddieee/pn_6d_single","url":"https://github.com/freddieee/pn_6d_single"},{"title":"ahmed-anas/thesis-pointnet","url":"https://github.com/ahmed-anas/thesis-pointnet"},{"title":"coconutzs/PointNet_zs","url":"https://github.com/coconutzs/PointNet_zs"},{"title":"CheesyB/cpointnet","url":"https://github.com/CheesyB/cpointnet"},{"title":"lingzhang1/pointnet_tensorflow","url":"https://github.com/lingzhang1/pointnet_tensorflow"},{"title":"aviros/pointnet_totations","url":"https://github.com/aviros/pointnet_totations"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106081,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Subvolume","metrics":{"Overall Accuracy":"89.2"},"paper_url":"http://arxiv.org/abs/1604.03265v2","paper_title":"Volumetric and Multi-View CNNs for Object Classification on 3D Data","paper_date":"2016-04-12","code_links":[{"title":"charlesq34/3dcnn.torch","url":"https://github.com/charlesq34/3dcnn.torch"},{"title":"LONG-9621/3DCNN","url":"https://github.com/LONG-9621/3DCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106082,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ECC","metrics":{"Mean Accuracy":"83.2","Overall Accuracy":"87.4"},"paper_url":"http://arxiv.org/abs/1704.02901v3","paper_title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","paper_date":"2017-04-10","code_links":[{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"mys007/ecc","url":"https://github.com/mys007/ecc"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106083,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"VRN (multiple views)","metrics":{"Mean Accuracy":"91.33"},"paper_url":"http://arxiv.org/abs/1608.04236v2","paper_title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","paper_date":"2016-08-15","code_links":[{"title":"ajbrock/Generative-and-Discriminative-Voxel-Modeling","url":"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106084,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"VRN (single view)","metrics":{"Mean Accuracy":"88.98"},"paper_url":"http://arxiv.org/abs/1608.04236v2","paper_title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","paper_date":"2016-08-15","code_links":[{"title":"ajbrock/Generative-and-Discriminative-Voxel-Modeling","url":"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106085,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"3DShapeNets","metrics":{"Mean Accuracy":"77.3"},"paper_url":"http://arxiv.org/abs/1406.5670v3","paper_title":"3D ShapeNets: A Deep Representation for Volumetric Shapes","paper_date":"2014-06-22","code_links":[{"title":"zhirongw/3DShapeNets","url":"https://github.com/zhirongw/3DShapeNets"},{"title":"hamidreza-dastmalchi/3dd-tta","url":"https://github.com/hamidreza-dastmalchi/3dd-tta"},{"title":"AliBahri94/SVWA_TTA","url":"https://github.com/AliBahri94/SVWA_TTA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106086,"task":"3D Point Cloud Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Perceiver","metrics":{"Mean Accuracy":"14.3"},"paper_url":"https://arxiv.org/abs/2103.03206v2","paper_title":"Perceiver: General Perception with Iterative Attention","paper_date":"2021-03-04","code_links":[{"title":"deepmind/deepmind-research","url":"https://github.com/deepmind/deepmind-research/tree/master/perceiver"},{"title":"towhee-io/towhee","url":"https://github.com/towhee-io/towhee"},{"title":"lucidrains/perceiver-pytorch","url":"https://github.com/lucidrains/perceiver-pytorch"},{"title":"krasserm/perceiver-io","url":"https://github.com/krasserm/perceiver-io"},{"title":"Rishit-dagli/Perceiver","url":"https://github.com/Rishit-dagli/Perceiver"},{"title":"louislva/deepmind-perceiver","url":"https://github.com/louislva/deepmind-perceiver"},{"title":"Xianchao-Wu/perceiver-pytorch","url":"https://github.com/Xianchao-Wu/perceiver-pytorch"},{"title":"fac2003/perceiver-multi-modality-pytorch","url":"https://github.com/fac2003/perceiver-multi-modality-pytorch"},{"title":"sooheon/perceiver-jax","url":"https://github.com/sooheon/perceiver-jax"},{"title":"clementpoiret/Perceiver_MNIST","url":"https://github.com/clementpoiret/Perceiver_MNIST"},{"title":"yangyucheng000/University","url":"https://github.com/yangyucheng000/University/tree/main/model-1/perceiver"},{"title":"kietngt00/hmdb51-recognition","url":"https://github.com/kietngt00/hmdb51-recognition"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146074,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointGST","metrics":{"Overall Accuracy":"95.3"},"paper_url":"https://arxiv.org/abs/2410.08114v1","paper_title":"Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning","paper_date":"2024-10-10","code_links":[{"title":"jerryfeng2003/pointgst","url":"https://github.com/jerryfeng2003/pointgst"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146075,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Mamba3D + Point-MAE","metrics":{"FLOPs":"3.9G","Number of params":"16.9M","Overall Accuracy":"95.1"},"paper_url":"https://arxiv.org/abs/2404.14966v2","paper_title":"Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model","paper_date":"2024-04-23","code_links":[{"title":"xhanxu/Mamba3D","url":"https://github.com/xhanxu/Mamba3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146076,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ReCon++","metrics":{"Overall Accuracy":"95.0"},"paper_url":"https://arxiv.org/abs/2402.17766v3","paper_title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","paper_date":"2024-02-27","code_links":[{"title":"qizekun/ShapeLLM","url":"https://github.com/qizekun/ShapeLLM"},{"title":"qizekun/ReCon","url":"https://github.com/qizekun/ReCon"},{"title":"runpeidong/act","url":"https://github.com/runpeidong/act"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146077,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointGPT","metrics":{"Overall Accuracy":"94.9"},"paper_url":null,"paper_title":null,"paper_date":null,"code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146078,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"point2vec","metrics":{"Mean Accuracy":"92.0","Overall Accuracy":"94.8"},"paper_url":"https://arxiv.org/abs/2303.16570v2","paper_title":"Point2Vec for Self-Supervised Representation Learning on Point Clouds","paper_date":"2023-03-29","code_links":[{"title":"kabouzeid/point2vec","url":"https://github.com/kabouzeid/point2vec"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146079,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ULIP + PointMLP","metrics":{"Mean Accuracy":"92.4","Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146080,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ReCon","metrics":{"Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2302.02318v2","paper_title":"Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining","paper_date":"2023-02-05","code_links":[{"title":"qizekun/ReCon","url":"https://github.com/qizekun/ReCon"},{"title":"runpeidong/act","url":"https://github.com/runpeidong/act"},{"title":"aHapBean/PCP-MAE","url":"https://github.com/aHapBean/PCP-MAE"},{"title":"asterisci/point-gcc","url":"https://github.com/asterisci/point-gcc"},{"title":"qizekun/vpp","url":"https://github.com/qizekun/vpp"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146081,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"AsymDSD-B* (no voting)","metrics":{"Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2506.21724v1","paper_title":"Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning","paper_date":"2025-06-26","code_links":[{"title":"RFLeijenaar/AsymDSD","url":"https://github.com/RFLeijenaar/AsymDSD"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146082,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RepSurf-U","metrics":{"FLOPs":"0.81G","Number of params":"1.48M","Overall Accuracy":"94.7"},"paper_url":"https://arxiv.org/abs/2205.05740v2","paper_title":"Surface Representation for Point Clouds","paper_date":"2022-05-11","code_links":[{"title":"hancyran/RepSurf","url":"https://github.com/hancyran/RepSurf"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146083,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMLP+HyCoRe","metrics":{"Mean Accuracy":"91.9","Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2209.10318v1","paper_title":"Rethinking the compositionality of point clouds through regularization in the hyperbolic space","paper_date":"2022-09-21","code_links":[{"title":"diegovalsesia/hycore","url":"https://github.com/diegovalsesia/hycore"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146084,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMLP","metrics":{"Mean Accuracy":"91.4","Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2202.07123v2","paper_title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","paper_date":"2022-02-15","code_links":[{"title":"ma-xu/pointmlp-pytorch","url":"https://github.com/ma-xu/pointmlp-pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146085,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-FEMAE","metrics":{"Overall Accuracy":"94.5"},"paper_url":"https://arxiv.org/abs/2312.10726v1","paper_title":"Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders","paper_date":"2023-12-17","code_links":[{"title":"zyh16143998882/aaai24-pointfemae","url":"https://github.com/zyh16143998882/aaai24-pointfemae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146086,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OTMae3D","metrics":{"Overall Accuracy":"94.5"},"paper_url":"https://ieeexplore.ieee.org/document/10815033","paper_title":"Rethinking Masked Representation Learning for 3D Point Cloud Understanding","paper_date":"2024-12-26","code_links":[{"title":"OpenSpaceAI/OTMae3D","url":"https://github.com/OpenSpaceAI/OTMae3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146087,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet2+PointCMT","metrics":{"Mean Accuracy":"91.2","Number of params":"1.62M","Overall Accuracy":"94.4"},"paper_url":"https://arxiv.org/abs/2210.04208v1","paper_title":"Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis","paper_date":"2022-10-09","code_links":[{"title":"yanx27/2dpass","url":"https://github.com/yanx27/2dpass"},{"title":"zhanheshen/pointcmt","url":"https://github.com/zhanheshen/pointcmt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146088,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"IDPT","metrics":{"Overall Accuracy":"94.4"},"paper_url":"https://arxiv.org/abs/2304.07221v2","paper_title":"Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models","paper_date":"2023-04-14","code_links":[{"title":"zyh16143998882/iccv23-idpt","url":"https://github.com/zyh16143998882/iccv23-idpt"},{"title":"zyh16143998882/IDPT","url":"https://github.com/zyh16143998882/IDPT"},{"title":"zyh16143998882/aaai24-pointfemae","url":"https://github.com/zyh16143998882/aaai24-pointfemae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146089,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OTMae3D (w/o Voting)","metrics":{"Overall Accuracy":"94.3"},"paper_url":"https://ieeexplore.ieee.org/document/10815033","paper_title":"Rethinking Masked Representation Learning for 3D Point Cloud Understanding","paper_date":"2024-12-26","code_links":[{"title":"OpenSpaceAI/OTMae3D","url":"https://github.com/OpenSpaceAI/OTMae3D"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146090,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"IAE + DGCNN","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2201.00785v5","paper_title":"Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning","paper_date":"2022-01-03","code_links":[{"title":"simingyan/iae","url":"https://github.com/simingyan/iae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146091,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PTv2","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2210.05666v2","paper_title":"Point Transformer V2: Grouped Vector Attention and Partition-based Pooling","paper_date":"2022-10-11","code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"Pointcept/PointTransformerV2","url":"https://github.com/Pointcept/PointTransformerV2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146092,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"CurveNet","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2105.01288v2","paper_title":"Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis","paper_date":"2021-05-04","code_links":[{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"tiangexiang/CurveNet","url":"https://github.com/tiangexiang/CurveNet"},{"title":"RF5/CurveNet","url":"https://github.com/RF5/CurveNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146093,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ExpPoint-MAE","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2306.10798v3","paper_title":"ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers","paper_date":"2023-06-19","code_links":[{"title":"vvrpanda/exppoint-mae","url":"https://github.com/vvrpanda/exppoint-mae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146094,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PCP-MAE","metrics":{"Overall Accuracy":"94.2"},"paper_url":"https://arxiv.org/abs/2408.08753v2","paper_title":"PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders","paper_date":"2024-08-16","code_links":[{"title":"aHapBean/PCP-MAE","url":"https://github.com/aHapBean/PCP-MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146095,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RPNet","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2108.12468v1","paper_title":"Learning Inner-Group Relations on Point Clouds","paper_date":"2021-08-27","code_links":[{"title":"hancyran/RPNet","url":"https://github.com/hancyran/RPNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146096,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ULIP + PointBERT","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146097,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-RAE","metrics":{"Overall Accuracy":"94.1"},"paper_url":"https://arxiv.org/abs/2310.03670v1","paper_title":"Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning","paper_date":"2023-09-25","code_links":[{"title":"liuyyy111/point-rae","url":"https://github.com/liuyyy111/point-rae"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146098,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-JEPA (voting)","metrics":{"Overall Accuracy":"94.1±0.1"},"paper_url":"https://arxiv.org/abs/2404.16432v5","paper_title":"Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud","paper_date":"2024-04-25","code_links":[{"title":"Ayumu-J-S/Point-JEPA","url":"https://github.com/Ayumu-J-S/Point-JEPA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146099,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DeLA","metrics":{"FLOPs":"1.44G","Mean Accuracy":"92.2","Number of params":"5.3M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2308.16532v1","paper_title":"Decoupled Local Aggregation for Point Cloud Learning","paper_date":"2023-08-31","code_links":[{"title":"matrix-asc/dela","url":"https://github.com/matrix-asc/dela"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146100,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"P2P","metrics":{"Mean Accuracy":"91.6","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2208.02812v2","paper_title":"P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting","paper_date":"2022-08-04","code_links":[{"title":"wangzy22/P2P","url":"https://github.com/wangzy22/P2P"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146101,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNeXt","metrics":{"FLOPs":"6.5G","Mean Accuracy":"91.1","Number of params":"4.5M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2206.04670v2","paper_title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","paper_date":"2022-06-09","code_links":[{"title":"guochengqian/pointnext","url":"https://github.com/guochengqian/pointnext"},{"title":"linhaojia13/pointmetabase","url":"https://github.com/linhaojia13/pointmetabase"},{"title":"boyden/pointtransformerfl","url":"https://github.com/boyden/pointtransformerfl"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146102,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point Voxel Transformer","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2108.06076v4","paper_title":"PVT: Point-Voxel Transformer for Point Cloud Learning","paper_date":"2021-08-13","code_links":[{"title":"HaochengWan/PVT","url":"https://github.com/HaochengWan/PVT"},{"title":"Mind23-2/MindCode-109","url":"https://github.com/Mind23-2/MindCode-109"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146103,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-MAE","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2203.06604v2","paper_title":"Masked Autoencoders for Point Cloud Self-supervised Learning","paper_date":"2022-03-13","code_links":[{"title":"Pang-Yatian/Point-MAE","url":"https://github.com/Pang-Yatian/Point-MAE"},{"title":"lmd0311/pointmamba","url":"https://github.com/lmd0311/pointmamba"},{"title":"vvrpanda/exppoint-mae","url":"https://github.com/vvrpanda/exppoint-mae"},{"title":"liujia99/tpm","url":"https://github.com/liujia99/tpm"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146104,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-M2AE","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2205.14401v2","paper_title":"Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training","paper_date":"2022-05-28","code_links":[{"title":"zrrskywalker/i2p-mae","url":"https://github.com/zrrskywalker/i2p-mae"},{"title":"zrrskywalker/point-m2ae","url":"https://github.com/zrrskywalker/point-m2ae"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146105,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMLS","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2401.08210v1","paper_title":"ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification","paper_date":"2024-01-16","code_links":[{"title":"fanglaosi/pointmls","url":"https://github.com/fanglaosi/pointmls"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146106,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3D-JEPA","metrics":{"Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2409.15803v1","paper_title":"3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning","paper_date":"2024-09-24","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146107,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"MKConv","metrics":{"Number of params":"5.63M","Overall Accuracy":"94.0"},"paper_url":"https://arxiv.org/abs/2107.12655v3","paper_title":"MKConv: Multidimensional Feature Representation for Point Cloud Analysis","paper_date":"2021-07-27","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146108,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SimpleView","metrics":{"Mean Accuracy":"91.8","Overall Accuracy":"93.9"},"paper_url":"https://openreview.net/forum?id=XwATtbX3oCz","paper_title":"Revisiting Point Cloud Classification with a Simple and Effective Baseline","paper_date":"2021-01-01","code_links":[{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"IsaacCorley/simpleview-pytorch","url":"https://github.com/IsaacCorley/simpleview-pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146109,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PAConv","metrics":{"Overall Accuracy":"93.9"},"paper_url":"https://arxiv.org/abs/2103.14635v2","paper_title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","paper_date":"2021-03-26","code_links":[{"title":"PaddlePaddle/Paddle3D","url":"https://github.com/PaddlePaddle/Paddle3D"},{"title":"CVMI-Lab/PAConv","url":"https://github.com/CVMI-Lab/PAConv"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146110,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SimpleView-DGCNN","metrics":{"Overall Accuracy":"93.9"},"paper_url":"https://arxiv.org/abs/2106.05304v1","paper_title":"Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline","paper_date":"2021-06-09","code_links":[{"title":"jiachens/ModelNet40-C","url":"https://github.com/jiachens/ModelNet40-C"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"axeber01/point-tnt","url":"https://github.com/axeber01/point-tnt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146111,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMAE+PPT","metrics":{"Overall Accuracy":"93.88"},"paper_url":"https://arxiv.org/abs/2408.11567v1","paper_title":"Positional Prompt Tuning for Efficient 3D Representation Learning","paper_date":"2024-08-21","code_links":[{"title":"zsc000722/ppt","url":"https://github.com/zsc000722/ppt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146112,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"MVTN","metrics":{"Mean Accuracy":"92.2","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2011.13244v3","paper_title":"MVTN: Multi-View Transformation Network for 3D Shape Recognition","paper_date":"2020-11-26","code_links":[{"title":"ajhamdi/MVTN","url":"https://github.com/ajhamdi/MVTN"},{"title":"ajhamdi/mvtorch","url":"https://github.com/ajhamdi/mvtorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146113,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Feature Geometric Net (FG-Net)","metrics":{"Mean Accuracy":"91.1","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2012.09439v2","paper_title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling","paper_date":"2020-12-17","code_links":[{"title":"KangchengLiu/Feature-Geometric-Net-FG-Net","url":"https://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146114,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"GBNet","metrics":{"Mean Accuracy":"91.0","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/1911.12885v5","paper_title":"Geometric Back-projection Network for Point Cloud Classification","paper_date":"2019-11-28","code_links":[{"title":"ShiQiu0419/GFNet","url":"https://github.com/ShiQiu0419/GFNet"},{"title":"ShiQiu0419/GBNet","url":"https://github.com/ShiQiu0419/GBNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146115,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DeltaConv","metrics":{"Mean class accuracy":"91.2","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2111.08799v5","paper_title":"DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds","paper_date":"2021-11-16","code_links":[{"title":"rubenwiersma/deltaconv","url":"https://github.com/rubenwiersma/deltaconv"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146116,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"GDANet","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2012.10921v3","paper_title":"Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud","paper_date":"2020-12-20","code_links":[{"title":"mutianxu/GDANet","url":"https://github.com/mutianxu/GDANet"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146117,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-BERT","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2111.14819v2","paper_title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","paper_date":"2021-11-29","code_links":[{"title":"julie-tang00/point-bert","url":"https://github.com/julie-tang00/point-bert"},{"title":"lulutang0608/Point-BERT","url":"https://github.com/lulutang0608/Point-BERT"},{"title":"lmd0311/pointmamba","url":"https://github.com/lmd0311/pointmamba"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146118,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"APES (global-based downsample)","metrics":{"Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2302.14673v2","paper_title":"Attention-based Point Cloud Edge Sampling","paper_date":"2023-02-28","code_links":[{"title":"JunweiZheng93/APES","url":"https://github.com/JunweiZheng93/APES"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146119,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-JEPA (no voting)","metrics":{"Overall Accuracy":"93.8±0.2"},"paper_url":"https://arxiv.org/abs/2404.16432v5","paper_title":"Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud","paper_date":"2024-04-25","code_links":[{"title":"Ayumu-J-S/Point-JEPA","url":"https://github.com/Ayumu-J-S/Point-JEPA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146120,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-PN","metrics":{"Number of params":"0.8M","Overall Accuracy":"93.8"},"paper_url":"https://arxiv.org/abs/2303.08134v2","paper_title":"Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis","paper_date":"2023-03-14","code_links":[{"title":"zrrskywalker/point-nn","url":"https://github.com/zrrskywalker/point-nn"},{"title":"opengvlab/cafo","url":"https://github.com/opengvlab/cafo"},{"title":"asalarpour/Point_GN","url":"https://github.com/asalarpour/Point_GN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146121,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointSCNet","metrics":{"Mean Accuracy":"91.4","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2202.10251v2","paper_title":"PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling","paper_date":"2022-02-21","code_links":[{"title":"Chenguoz/PointSCNet","url":"https://github.com/Chenguoz/PointSCNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146122,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PRA-Net","metrics":{"Mean Accuracy":"91.2","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2112.04903v1","paper_title":"PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis","paper_date":"2021-12-09","code_links":[{"title":"xiwuchen/pra-net","url":"https://github.com/xiwuchen/pra-net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146123,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointTransformer","metrics":{"Mean Accuracy":"90.6","Overall Accuracy":"93.7"},"paper_url":"https://arxiv.org/abs/2012.09164v2","paper_title":"Point Transformer","paper_date":"2020-12-16","code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"lucidrains/point-transformer-pytorch","url":"https://github.com/lucidrains/point-transformer-pytorch"},{"title":"POSTECH-CVLab/point-transformer","url":"https://github.com/POSTECH-CVLab/point-transformer"},{"title":"gofinge/pointtransformerv2","url":"https://github.com/gofinge/pointtransformerv2"},{"title":"POSTECH-CVLab/FastPointTransformer","url":"https://github.com/POSTECH-CVLab/FastPointTransformer"},{"title":"lifebeyondexpectations/eccv22-pointmixer","url":"https://github.com/lifebeyondexpectations/eccv22-pointmixer"},{"title":"engelnico/point-transformer","url":"https://github.com/engelnico/point-transformer"},{"title":"Sharpiless/Point-Transformer-Pytorch","url":"https://github.com/Sharpiless/Point-Transformer-Pytorch"},{"title":"Meowuu7/Point-Transformer","url":"https://github.com/Meowuu7/Point-Transformer"},{"title":"code-implementation1/Code6","url":"https://github.com/code-implementation1/Code6/tree/main/pointtransformer"},{"title":"jxl152/Point-Transformer","url":"https://github.com/jxl152/Point-Transformer"},{"title":"rauleun/point-transformer-tf2","url":"https://github.com/rauleun/point-transformer-tf2"},{"title":"MS-Mind/MS-Code-06","url":"https://github.com/MS-Mind/MS-Code-06/tree/main/pointtransformer"},{"title":"KernelA/pytorch-point-transformer","url":"https://github.com/KernelA/pytorch-point-transformer"},{"title":"tkdguraa/point-transformer-tensorflow","url":"https://github.com/tkdguraa/point-transformer-tensorflow"},{"title":"sh4175515/PointTransformer","url":"https://github.com/sh4175515/PointTransformer"},{"title":"alzmzyy/pointtransformer","url":"https://github.com/alzmzyy/pointtransformer"},{"title":"Mind23-2/MindCode-5","url":"https://github.com/Mind23-2/MindCode-5/tree/main/pointtransformer"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"2023-MindSpore-1/ms-code-215","url":"https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer"},{"title":"2023-MindSpore-1/ms-code-48","url":"https://github.com/2023-MindSpore-1/ms-code-48"},{"title":"MindSpore-paper-code-2/code2","url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146124,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DualMLP","metrics":{"Overall Accuracy":"93.7"},"paper_url":"https://link.springer.com/article/10.1007/s00371-023-03114-3","paper_title":"DualMLP: a two-stream fusion model for 3D point cloud classification","paper_date":"2023-10-10","code_links":[{"title":"snehaputul/DualMLP","url":"https://github.com/snehaputul/DualMLP"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146125,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointMixer","metrics":{"Mean Accuracy":"91.4","Number of params":"6.5M","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2111.11187v5","paper_title":"PointMixer: MLP-Mixer for Point Cloud Understanding","paper_date":"2021-11-22","code_links":[{"title":"lifebeyondexpectations/eccv22-pointmixer","url":"https://github.com/lifebeyondexpectations/eccv22-pointmixer"},{"title":"LifeBeyondExpectations/PointMixer","url":"https://github.com/LifeBeyondExpectations/PointMixer"},{"title":"hrisi/tree-species-classification","url":"https://github.com/hrisi/tree-species-classification"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146126,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DeepGCN","metrics":{"Mean Accuracy":"90.9","Number of params":"2.2M","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/1910.06849v3","paper_title":"DeepGCNs: Making GCNs Go as Deep as CNNs","paper_date":"2019-10-15","code_links":[{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb"},{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_arxiv_dgl"},{"title":"lightaime/deep_gcns_torch","url":"https://github.com/lightaime/deep_gcns_torch"},{"title":"lightaime/deep_gcns","url":"https://github.com/lightaime/deep_gcns"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146127,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"LCPFormer","metrics":{"Mean Accuracy":"90.7","Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2210.12755v2","paper_title":"LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers","paper_date":"2022-10-23","code_links":[{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146128,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DGCNN + SageMix","metrics":{"Overall Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146129,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointVector-S","metrics":{"Mean Accuracy":"91","Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2205.10528v3","paper_title":"PointVector: A Vector Representation In Point Cloud Analysis","paper_date":"2022-05-21","code_links":[{"title":"guochengqian/openpoints","url":"https://github.com/guochengqian/openpoints/blob/master/models/backbone/pointvector.py"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146130,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RSMix","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2102.01929v3","paper_title":"Regularization Strategy for Point Cloud via Rigidly Mixed Sample","paper_date":"2021-02-03","code_links":[{"title":"dogyoonlee/RSMix-official","url":"https://github.com/dogyoonlee/RSMix-official"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146131,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointConT","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2303.04599v1","paper_title":"Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space","paper_date":"2023-03-08","code_links":[{"title":"yahuiliu99/pointcont","url":"https://github.com/yahuiliu99/pointcont"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146132,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"APES (local-based downsample)","metrics":{"Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2302.14673v2","paper_title":"Attention-based Point Cloud Edge Sampling","paper_date":"2023-02-28","code_links":[{"title":"JunweiZheng93/APES","url":"https://github.com/JunweiZheng93/APES"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146133,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DSPoint","metrics":{"Number of params":"1.16M","Overall Accuracy":"93.5"},"paper_url":"https://arxiv.org/abs/2111.10332v4","paper_title":"DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion","paper_date":"2021-11-19","code_links":[{"title":"adonis-galaxy/dspoint","url":"https://github.com/adonis-galaxy/dspoint"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146134,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ULIP + PointNet++(ssg)","metrics":{"Mean Accuracy":"91.2","Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","paper_date":"2022-12-10","code_links":[{"title":"salesforce/ulip","url":"https://github.com/salesforce/ulip"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146135,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointCutmix","metrics":{"Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2101.01461v2","paper_title":"PointCutMix: Regularization Strategy for Point Cloud Classification","paper_date":"2021-01-05","code_links":[{"title":"jiachens/ModelNet40-C","url":"https://github.com/jiachens/ModelNet40-C"},{"title":"cuge1995/PointCutMix","url":"https://github.com/cuge1995/PointCutMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146136,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3DMedPT","metrics":{"Number of params":"1.54M","Overall Accuracy":"93.4"},"paper_url":"https://arxiv.org/abs/2112.04863v2","paper_title":"3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis","paper_date":"2021-12-09","code_links":[{"title":"crane-papercode/3dmedpt","url":"https://github.com/crane-papercode/3dmedpt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146137,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DGCNN + MD","metrics":{"Mean Accuracy":"89.88","Overall Accuracy":"93.39"},"paper_url":"https://arxiv.org/abs/2109.00452v3","paper_title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","paper_date":"2021-09-01","code_links":[{"title":"cyysc1998/3D-Pretraining","url":"https://github.com/cyysc1998/3D-Pretraining"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146138,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OGNet + MD","metrics":{"Mean Accuracy":"90.71","Overall Accuracy":"93.31"},"paper_url":"https://arxiv.org/abs/2109.00452v3","paper_title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","paper_date":"2021-09-01","code_links":[{"title":"cyysc1998/3D-Pretraining","url":"https://github.com/cyysc1998/3D-Pretraining"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146139,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"OG-Net-Small","metrics":{"Mean Accuracy":"90.5","Number of params":"1.22M","Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2006.04569v3","paper_title":"Parameter-Efficient Person Re-identification in the 3D Space","paper_date":"2020-06-08","code_links":[{"title":"layumi/person-reid-3d","url":"https://github.com/layumi/person-reid-3d"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146140,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointStack","metrics":{"Mean Accuracy":"89.6","Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2205.09962v1","paper_title":"Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling","paper_date":"2022-05-20","code_links":[{"title":"LongerVision/PointStack","url":"https://github.com/LongerVision/PointStack"},{"title":"kaist-avelab/PointStack","url":"https://github.com/kaist-avelab/PointStack"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146141,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet++ + SageMix","metrics":{"Overall Accuracy":"93.3"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146142,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointASNL","metrics":{"Overall Accuracy":"93.2"},"paper_url":"https://arxiv.org/abs/2003.00492v3","paper_title":"PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling","paper_date":"2020-03-01","code_links":[{"title":"yanx27/PointASNL","url":"https://github.com/yanx27/PointASNL"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146143,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point Cloud Transformer","metrics":{"Number of params":"2.88M","Overall Accuracy":"93.2"},"paper_url":"https://arxiv.org/abs/2012.09688v4","paper_title":"PCT: Point cloud transformer","paper_date":"2020-12-17","code_links":[{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"MenghaoGuo/PCT","url":"https://github.com/MenghaoGuo/PCT"},{"title":"Strawberry-Eat-Mango/PCT_Pytorch","url":"https://github.com/Strawberry-Eat-Mango/PCT_Pytorch"},{"title":"uyzhang/PCT_Pytorch","url":"https://github.com/uyzhang/PCT_Pytorch"},{"title":"uuyzhang/PCT_Pytorch","url":"https://github.com/uuyzhang/PCT_Pytorch"},{"title":"irmvlab/point-mamba","url":"https://github.com/irmvlab/point-mamba"},{"title":"qinglew/PointCloudTransformer","url":"https://github.com/qinglew/PointCloudTransformer"},{"title":"Liu-Feng/PCT-tensorflow","url":"https://github.com/Liu-Feng/PCT-tensorflow"},{"title":"ViniciusMikuni/PCT_HEP","url":"https://github.com/ViniciusMikuni/PCT_HEP"},{"title":"toothfairy42/PCT-pytorch","url":"https://github.com/toothfairy42/PCT-pytorch"},{"title":"DLW3D/PCT_Pytorch","url":"https://github.com/DLW3D/PCT_Pytorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146144,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DRNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/2005.06734v2","paper_title":"Dense-Resolution Network for Point Cloud Classification and Segmentation","paper_date":"2020-05-14","code_links":[{"title":"ShiQiu0419/DRNet","url":"https://github.com/ShiQiu0419/DRNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146145,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ShellNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/1908.06295v1","paper_title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","paper_date":"2019-08-17","code_links":[{"title":"hkust-vgd/shellnet","url":"https://github.com/hkust-vgd/shellnet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146146,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"STRL + DGCNN","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://arxiv.org/abs/2109.00179v1","paper_title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","paper_date":"2021-09-01","code_links":[{"title":"yichen928/STRL","url":"https://github.com/yichen928/STRL"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146147,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PatchAugment+DGCNN","metrics":{"Overall Accuracy":"93.1"},"paper_url":null,"paper_title":null,"paper_date":null,"code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146148,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"AdaCrossNet","metrics":{"Overall Accuracy":"93.1"},"paper_url":"https://inass.org/wp-content/uploads/2024/10/2025022911-2.pdf","paper_title":"AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning","paper_date":"2025-01-02","code_links":[{"title":"virgantara/AdaCrossNet","url":"https://github.com/virgantara/AdaCrossNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146149,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointManifold","metrics":{"Mean Accuracy":"90.4","Overall Accuracy":"93.0"},"paper_url":"https://arxiv.org/abs/2010.07215v2","paper_title":"PointManifold: Using Manifold Learning for Point Cloud Classification","paper_date":"2020-10-14","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146150,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"InterpCNN","metrics":{"Overall Accuracy":"93.0"},"paper_url":"https://arxiv.org/abs/1908.04512v1","paper_title":"Interpolated Convolutional Networks for 3D Point Cloud Understanding","paper_date":"2019-08-13","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146151,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DGCNN","metrics":{"Mean Accuracy":"90.2","Number of params":"1.81M","Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1801.07829v2","paper_title":"Dynamic Graph CNN for Learning on Point Clouds","paper_date":"2018-01-24","code_links":[{"title":"WangYueFt/dgcnn","url":"https://github.com/WangYueFt/dgcnn"},{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"AnTao97/dgcnn.pytorch","url":"https://github.com/AnTao97/dgcnn.pytorch"},{"title":"bupt-gamma/gammagl","url":"https://github.com/bupt-gamma/gammagl"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"hqucms/ParticleNet","url":"https://github.com/hqucms/ParticleNet"},{"title":"lingzhang1/ContrastNet","url":"https://github.com/lingzhang1/ContrastNet"},{"title":"cy-xu/spatially_aware_ai","url":"https://github.com/cy-xu/spatially_aware_ai"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"},{"title":"hansen7/NRS_3D","url":"https://github.com/hansen7/NRS_3D"},{"title":"brent-murray/tr3d_pointaugdgcnn","url":"https://github.com/brent-murray/tr3d_pointaugdgcnn"},{"title":"nnn911/MLSI","url":"https://github.com/nnn911/MLSI"},{"title":"lingzhang1/dgcnn_sampling","url":"https://github.com/lingzhang1/dgcnn_sampling"},{"title":"lingzhang1/dgcnn_v2","url":"https://github.com/lingzhang1/dgcnn_v2"},{"title":"xrndai/dgcnn_amino_acid_structure","url":"https://github.com/xrndai/dgcnn_amino_acid_structure"},{"title":"JingfeiHuang/DGCNN-Paddle","url":"https://github.com/JingfeiHuang/DGCNN-Paddle"},{"title":"af13s/dgcnn-amino","url":"https://github.com/af13s/dgcnn-amino"},{"title":"Squanch-U/DGCNN","url":"https://github.com/Squanch-U/DGCNN"},{"title":"AmitBracha/GIP_project","url":"https://github.com/AmitBracha/GIP_project"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146152,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"KPConv","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1904.08889v2","paper_title":"KPConv: Flexible and Deformable Convolution for Point Clouds","paper_date":"2019-04-18","code_links":[{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"HuguesTHOMAS/KPConv-PyTorch","url":"https://github.com/HuguesTHOMAS/KPConv-PyTorch"},{"title":"HuguesTHOMAS/KPConv","url":"https://github.com/HuguesTHOMAS/KPConv"},{"title":"ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D"},{"title":"XuyangBai/KPConv.pytorch","url":"https://github.com/XuyangBai/KPConv.pytorch"},{"title":"plusmultiply/mprm","url":"https://github.com/plusmultiply/mprm"},{"title":"Arjun-NA/KPConv_for_DALES","url":"https://github.com/Arjun-NA/KPConv_for_DALES"},{"title":"Yacovitch/EyeNet","url":"https://github.com/Yacovitch/EyeNet"},{"title":"genglinliu/KPConv_Pytorch","url":"https://github.com/genglinliu/KPConv_Pytorch"},{"title":"JohnRomanelis/KPConv_torch_geometric","url":"https://github.com/JohnRomanelis/KPConv_torch_geometric"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146153,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"RS-CNN","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/1904.07601v3","paper_title":"Relation-Shape Convolutional Neural Network for Point Cloud Analysis","paper_date":"2019-04-16","code_links":[{"title":"Yochengliu/Relation-Shape-CNN","url":"https://github.com/Yochengliu/Relation-Shape-CNN"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"sausagecy/RSCNN_Pytorch1.0","url":"https://github.com/sausagecy/RSCNN_Pytorch1.0"},{"title":"panyunyi97/RSCNN","url":"https://github.com/panyunyi97/RSCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146154,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ASSANet","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/2110.10538v2","paper_title":"ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning","paper_date":"2021-10-20","code_links":[{"title":"guochengqian/assanet","url":"https://github.com/guochengqian/assanet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146155,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-M2AE-SVM","metrics":{"Overall Accuracy":"92.9"},"paper_url":"https://arxiv.org/abs/2205.14401v2","paper_title":"Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training","paper_date":"2022-05-28","code_links":[{"title":"zrrskywalker/i2p-mae","url":"https://github.com/zrrskywalker/i2p-mae"},{"title":"zrrskywalker/point-m2ae","url":"https://github.com/zrrskywalker/point-m2ae"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146156,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point Transformer","metrics":{"Overall Accuracy":"92.8"},"paper_url":"https://arxiv.org/abs/2011.00931v2","paper_title":"Point Transformer","paper_date":"2020-11-02","code_links":[{"title":"qq456cvb/Point-Transformers","url":"https://github.com/qq456cvb/Point-Transformers"},{"title":"engelnico/point-transformer","url":"https://github.com/engelnico/point-transformer"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146157,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"A-CNN","metrics":{"Overall Accuracy":"92.6"},"paper_url":"http://arxiv.org/abs/1904.08017v1","paper_title":"A-CNN: Annularly Convolutional Neural Networks on Point Clouds","paper_date":"2019-04-16","code_links":[{"title":"artemkomarichev/a-cnn","url":"https://github.com/artemkomarichev/a-cnn"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146158,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"P2Sequence","metrics":{"Overall Accuracy":"92.6"},"paper_url":"http://arxiv.org/abs/1811.02565v2","paper_title":"Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network","paper_date":"2018-11-06","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146159,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-TnT","metrics":{"Number of params":"3.9M","Overall Accuracy":"92.6"},"paper_url":"https://arxiv.org/abs/2204.03957v1","paper_title":"Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition","paper_date":"2022-04-08","code_links":[{"title":"axeber01/point-tnt","url":"https://github.com/axeber01/point-tnt"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146160,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointConv","metrics":{"Overall Accuracy":"92.5"},"paper_url":"https://arxiv.org/abs/1811.07246v3","paper_title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","paper_date":"2018-11-17","code_links":[{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"DylanWusee/pointconv","url":"https://github.com/DylanWusee/pointconv"},{"title":"DylanWusee/pointconv_pytorch","url":"https://github.com/DylanWusee/pointconv_pytorch"},{"title":"dgriffiths3/pointconv-tensorflow2","url":"https://github.com/dgriffiths3/pointconv-tensorflow2"},{"title":"Young98CN/pointconv_pytorch","url":"https://github.com/Young98CN/pointconv_pytorch"},{"title":"Ghailen-Ben-Achour/PointConv_segmentation","url":"https://github.com/Ghailen-Ben-Achour/PointConv_segmentation"},{"title":"koritsky/pointconv","url":"https://github.com/koritsky/pointconv"},{"title":"THHHomas/mls","url":"https://github.com/THHHomas/mls"},{"title":"ECE685-FinalProject/3D-object-recognition","url":"https://github.com/ECE685-FinalProject/3D-object-recognition"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146161,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PolyNet","metrics":{"Overall Accuracy":"92.42"},"paper_url":"https://arxiv.org/abs/2110.07882v1","paper_title":"PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation","paper_date":"2021-10-15","code_links":[{"title":"myavartanoo/PolyNet_PyTorch","url":"https://github.com/myavartanoo/PolyNet_PyTorch"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146162,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SpiderCNN","metrics":{"Overall Accuracy":"92.4"},"paper_url":"http://arxiv.org/abs/1803.11527v3","paper_title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","paper_date":"2018-03-30","code_links":[{"title":"xyf513/SpiderCNN","url":"https://github.com/xyf513/SpiderCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146163,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PCNN","metrics":{"Overall Accuracy":"92.3"},"paper_url":"http://arxiv.org/abs/1803.10091v1","paper_title":"Point Convolutional Neural Networks by Extension Operators","paper_date":"2018-03-27","code_links":[{"title":"matanatz/pcnn","url":"https://github.com/matanatz/pcnn"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146164,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointCNN","metrics":{"Overall Accuracy":"92.2"},"paper_url":"http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points","paper_title":"PointCNN: Convolution On X-Transformed Points","paper_date":"2018-12-01","code_links":[{"title":"yangyanli/PointCNN","url":"https://github.com/yangyanli/PointCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146165,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Point-PlaneNet","metrics":{"Mean Accuracy":"90.5","Overall Accuracy":"92.1"},"paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S1051200419301873","paper_title":"Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis","paper_date":"2020-05-01","code_links":[{"title":"moeinp70/Point-Planenet","url":"https://github.com/moeinp70/Point-Planenet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146166,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SpecGCN","metrics":{"Overall Accuracy":"92.1"},"paper_url":"http://arxiv.org/abs/1803.05827v1","paper_title":"Local Spectral Graph Convolution for Point Set Feature Learning","paper_date":"2018-03-15","code_links":[{"title":"fate3439/LocalSpecGCN","url":"https://github.com/fate3439/LocalSpecGCN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146167,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"DynamicScale","metrics":{"Overall Accuracy":"92.1"},"paper_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9565556","paper_title":"Dynamic Local Geometry Capture in 3D PointCloud Classification","paper_date":"2021-10-19","code_links":[{"title":"VimsLab/DynamicScale","url":"https://github.com/VimsLab/DynamicScale"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146168,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointGrid","metrics":{"Overall Accuracy":"92.0"},"paper_url":"http://openaccess.thecvf.com/content_cvpr_2018/html/Le_PointGrid_A_Deep_CVPR_2018_paper.html","paper_title":"PointGrid: A Deep Network for 3D Shape Understanding","paper_date":"2018-06-01","code_links":[{"title":"trucleduc/PointGrid","url":"https://github.com/trucleduc/PointGrid"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146169,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Kd-Net","metrics":{"Overall Accuracy":"91.8"},"paper_url":"http://arxiv.org/abs/1704.01222v2","paper_title":"Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models","paper_date":"2017-04-04","code_links":[{"title":"fxia22/kdnet.pytorch","url":"https://github.com/fxia22/kdnet.pytorch"},{"title":"qq456cvb/KdNet-Tensorflow","url":"https://github.com/qq456cvb/KdNet-Tensorflow"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146170,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Overall Accuracy":"91.7"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/8354322","paper_title":"General-Purpose Deep Point Cloud Feature Extractor","paper_date":"2018-03-12","code_links":[{"title":"WDot/G3DNet","url":"https://github.com/WDot/G3DNet"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146171,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3DMFV-Net","metrics":{"Overall Accuracy":"91.6"},"paper_url":"http://arxiv.org/abs/1711.08241v1","paper_title":"3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks","paper_date":"2017-11-22","code_links":[{"title":"sitzikbs/3DmFV-Net","url":"https://github.com/sitzikbs/3DmFV-Net"},{"title":"sitzikbs/3DmFV-Net-MATLAB","url":"https://github.com/sitzikbs/3DmFV-Net-MATLAB"},{"title":"fferroni/fisher_vector_classifier","url":"https://github.com/fferroni/fisher_vector_classifier"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146172,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Overall Accuracy":"91.49"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/10229841","paper_title":"CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud","paper_date":"2023-06-08","code_links":[{"title":"snehaputul/CrossMoCo","url":"https://github.com/snehaputul/CrossMoCo"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146173,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"SO-Net","metrics":{"Overall Accuracy":"90.9"},"paper_url":"http://arxiv.org/abs/1803.04249v4","paper_title":"SO-Net: Self-Organizing Network for Point Cloud Analysis","paper_date":"2018-03-12","code_links":[{"title":"lijx10/SO-Net","url":"https://github.com/lijx10/SO-Net"},{"title":"donnyruixu/pc-elm-ae","url":"https://github.com/donnyruixu/pc-elm-ae"},{"title":"LONG-9621/SO-Net","url":"https://github.com/LONG-9621/SO-Net"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146174,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet++","metrics":{"Number of params":"1.74M","Overall Accuracy":"90.7"},"paper_url":"http://arxiv.org/abs/1706.02413v1","paper_title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","paper_date":"2017-06-07","code_links":[{"title":"yanx27/Pointnet_Pointnet2_pytorch","url":"https://github.com/yanx27/Pointnet_Pointnet2_pytorch"},{"title":"charlesq34/pointnet2","url":"https://github.com/charlesq34/pointnet2"},{"title":"facebookresearch/votenet","url":"https://github.com/facebookresearch/votenet"},{"title":"erikwijmans/Pointnet2_PyTorch","url":"https://github.com/erikwijmans/Pointnet2_PyTorch"},{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"sshaoshuai/Pointnet2.PyTorch","url":"https://github.com/sshaoshuai/Pointnet2.PyTorch"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/pointnet2"},{"title":"zaiweizhang/H3DNet","url":"https://github.com/zaiweizhang/H3DNet"},{"title":"hehefan/P4Transformer","url":"https://github.com/hehefan/P4Transformer"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"thu17cyz/3DIoUMatch","url":"https://github.com/thu17cyz/3DIoUMatch"},{"title":"hehefan/PointRNN","url":"https://github.com/hehefan/PointRNN"},{"title":"facebookresearch/imvotenet","url":"https://github.com/facebookresearch/imvotenet"},{"title":"referit3d/referit3d","url":"https://github.com/referit3d/referit3d"},{"title":"hehefan/Point-Spatio-Temporal-Convolution","url":"https://github.com/hehefan/Point-Spatio-Temporal-Convolution"},{"title":"dgriffiths3/pointnet2-tensorflow2","url":"https://github.com/dgriffiths3/pointnet2-tensorflow2"},{"title":"nickgkan/beauty_detr","url":"https://github.com/nickgkan/beauty_detr"},{"title":"nickgkan/butd_detr","url":"https://github.com/nickgkan/butd_detr"},{"title":"open-air-sun/pq-transformer","url":"https://github.com/open-air-sun/pq-transformer"},{"title":"hehefan/PointRNN-PyTorch","url":"https://github.com/hehefan/PointRNN-PyTorch"},{"title":"joyhsu0504/ns3d","url":"https://github.com/joyhsu0504/ns3d"},{"title":"curryyuan/x-trans2cap","url":"https://github.com/curryyuan/x-trans2cap"},{"title":"zyang-ur/SAT","url":"https://github.com/zyang-ur/SAT"},{"title":"iballester/SPiKE","url":"https://github.com/iballester/SPiKE"},{"title":"johnsonsign/mast-pre","url":"https://github.com/johnsonsign/mast-pre"},{"title":"AsahiLiu/PointDetectron","url":"https://github.com/AsahiLiu/PointDetectron"},{"title":"hehefan/PSTNet2","url":"https://github.com/hehefan/PSTNet2"},{"title":"JohnsonSign/PointCMP","url":"https://github.com/JohnsonSign/PointCMP"},{"title":"dfki-av/mikasa-3dvg","url":"https://github.com/dfki-av/mikasa-3dvg"},{"title":"Ghailen-Ben-Achour/PointNet2_Segmentation","url":"https://github.com/Ghailen-Ben-Achour/PointNet2_Segmentation"},{"title":"houseleo/pointnet","url":"https://github.com/houseleo/pointnet"},{"title":"tao-tao-tao-tao-tao/diffusion_suction","url":"https://github.com/tao-tao-tao-tao-tao/diffusion_suction"},{"title":"Lonepic/SPIB","url":"https://github.com/Lonepic/SPIB"},{"title":"asalarpour/Point_GN","url":"https://github.com/asalarpour/Point_GN"},{"title":"LinZhuoChen/pointnet2_multi_gpu","url":"https://github.com/LinZhuoChen/pointnet2_multi_gpu"},{"title":"shaygu62/Protein-Classification","url":"https://github.com/shaygu62/Protein-Classification"},{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"},{"title":"ikh-innovation/roboweldar-votenet","url":"https://github.com/ikh-innovation/roboweldar-votenet"},{"title":"witignite/Frustum-PointNet","url":"https://github.com/witignite/Frustum-PointNet"},{"title":"airalcorn2/parking-lot-pointnetplusplus","url":"https://github.com/airalcorn2/parking-lot-pointnetplusplus"},{"title":"ftdlyc/pointnet_pytorch","url":"https://github.com/ftdlyc/pointnet_pytorch"},{"title":"tingxueronghua/dpke","url":"https://github.com/tingxueronghua/dpke"},{"title":"timothylimyl/PointNet-Pytorch","url":"https://github.com/timothylimyl/PointNet-Pytorch"},{"title":"zhh6425/MotionPointNet","url":"https://github.com/zhh6425/MotionPointNet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/ascend_pointnet2"},{"title":"hoangcuongbk80/VoteGrasp","url":"https://github.com/hoangcuongbk80/VoteGrasp"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet2"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/PointNet2"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/PointNet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/gpu_pointnet2"},{"title":"tonysy/pointnet2_tf","url":"https://github.com/tonysy/pointnet2_tf"},{"title":"Xiangxu-0103/Octant-CNN","url":"https://github.com/Xiangxu-0103/Octant-CNN"},{"title":"Lw510107/PointNet","url":"https://github.com/Lw510107/PointNet"},{"title":"xurui1217/pointnet2-master","url":"https://github.com/xurui1217/pointnet2-master"},{"title":"SY-Xuan/pointnet_plus_plus_paddlepaddle","url":"https://github.com/SY-Xuan/pointnet_plus_plus_paddlepaddle"},{"title":"LONG-9621/VoteNet","url":"https://github.com/LONG-9621/VoteNet"},{"title":"LONG-9621/PointNet-","url":"https://github.com/LONG-9621/PointNet-"},{"title":"Harut0726/votenet","url":"https://github.com/Harut0726/votenet"},{"title":"FlowWind1999/pointnet-2","url":"https://github.com/FlowWind1999/pointnet-2"},{"title":"llzlcl/pointcloud-segment","url":"https://github.com/llzlcl/pointcloud-segment"},{"title":"zenroad/modifypointnet","url":"https://github.com/zenroad/modifypointnet"},{"title":"caizeyu1992/pointnet2","url":"https://github.com/caizeyu1992/pointnet2"},{"title":"2023-MindSpore-1/ms-code-8","url":"https://github.com/2023-MindSpore-1/ms-code-8/tree/main/PointNet2"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"2023-MindSpore-1/ms-code-223","url":"https://github.com/2023-MindSpore-1/ms-code-223/tree/main/PointNet"},{"title":"MindSpore-paper-code-3/code4","url":"https://github.com/MindSpore-paper-code-3/code4/tree/main/frustum-pointnet"},{"title":"2023-MindSpore-1/ms-code-223","url":"https://github.com/2023-MindSpore-1/ms-code-223/tree/main/PointNet2"},{"title":"brbzjl/pointnet2","url":"https://github.com/brbzjl/pointnet2"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146175,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Kd-net","metrics":{"Overall Accuracy":"90.6"},"paper_url":"http://arxiv.org/abs/1704.01222v2","paper_title":"Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models","paper_date":"2017-04-04","code_links":[{"title":"fxia22/kdnet.pytorch","url":"https://github.com/fxia22/kdnet.pytorch"},{"title":"qq456cvb/KdNet-Tensorflow","url":"https://github.com/qq456cvb/KdNet-Tensorflow"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146176,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet + SageMix","metrics":{"Overall Accuracy":"90.3"},"paper_url":"https://arxiv.org/abs/2210.06944v1","paper_title":"SageMix: Saliency-Guided Mixup for Point Clouds","paper_date":"2022-10-13","code_links":[{"title":"mlvlab/SageMix","url":"https://github.com/mlvlab/SageMix"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146177,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"MVCNN","metrics":{"Overall Accuracy":"90.1"},"paper_url":"http://arxiv.org/abs/1505.00880v3","paper_title":"Multi-view Convolutional Neural Networks for 3D Shape Recognition","paper_date":"2015-05-05","code_links":[],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146178,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"PointNet","metrics":{"Mean Accuracy":"86.0","Number of params":"3.47M","Overall Accuracy":"89.2"},"paper_url":"http://arxiv.org/abs/1612.00593v2","paper_title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","paper_date":"2016-12-02","code_links":[{"title":"charlesq34/pointnet","url":"https://github.com/charlesq34/pointnet"},{"title":"fxia22/pointnet.pytorch","url":"https://github.com/fxia22/pointnet.pytorch"},{"title":"WangYueFt/dgcnn","url":"https://github.com/WangYueFt/dgcnn"},{"title":"vinits5/learning3d","url":"https://github.com/vinits5/learning3d"},{"title":"DylanWusee/pointconv_pytorch","url":"https://github.com/DylanWusee/pointconv_pytorch"},{"title":"nikitakaraevv/pointnet","url":"https://github.com/nikitakaraevv/pointnet"},{"title":"maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification","url":"https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification"},{"title":"princeton-vl/SimpleView","url":"https://github.com/princeton-vl/SimpleView"},{"title":"sarthakTUM/roofn3d","url":"https://github.com/sarthakTUM/roofn3d"},{"title":"jtpils/treegan","url":"https://github.com/jtpils/treegan"},{"title":"ajhamdi/AdvPC","url":"https://github.com/ajhamdi/AdvPC"},{"title":"soumik12345/point-cloud-segmentation","url":"https://github.com/soumik12345/point-cloud-segmentation"},{"title":"AI-Guru/pointcloud_experiments","url":"https://github.com/AI-Guru/pointcloud_experiments"},{"title":"ali-stanford/pointnetcfd","url":"https://github.com/ali-stanford/pointnetcfd"},{"title":"romaintha/pytorch_pointnet","url":"https://github.com/romaintha/pytorch_pointnet"},{"title":"saaries/PointNet","url":"https://github.com/saaries/PointNet"},{"title":"Fragjacker/Pointcloud-grad-CAM","url":"https://github.com/Fragjacker/Pointcloud-grad-CAM"},{"title":"ZhihaoZhu/PointNet-Implementation-Tensorflow","url":"https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow"},{"title":"YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti","url":"https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti"},{"title":"Yuto0107/pointnet","url":"https://github.com/Yuto0107/pointnet"},{"title":"YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024","url":"https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024"},{"title":"opeco17/pointnet","url":"https://github.com/opeco17/pointnet"},{"title":"Dir-b/PointNet","url":"https://github.com/Dir-b/PointNet"},{"title":"Young98CN/pointconv_pytorch","url":"https://github.com/Young98CN/pointconv_pytorch"},{"title":"y2kmz/pointnetv2","url":"https://github.com/y2kmz/pointnetv2"},{"title":"YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization","url":"https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization"},{"title":"imanlab/deep_movement_primitives","url":"https://github.com/imanlab/deep_movement_primitives"},{"title":"abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds","url":"https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds"},{"title":"donshen/pointnet.phasedetection","url":"https://github.com/donshen/pointnet.phasedetection"},{"title":"ModelBunker/PointNet-TensorFlow","url":"https://github.com/ModelBunker/PointNet-TensorFlow"},{"title":"amyllykoski/CycleGAN","url":"https://github.com/amyllykoski/CycleGAN"},{"title":"Javed002/Object_detection_PointNet","url":"https://github.com/Javed002/Object_detection_PointNet"},{"title":"sanantoniochili/PointCloud_KNN","url":"https://github.com/sanantoniochili/PointCloud_KNN"},{"title":"PaParaZz1/PointNet","url":"https://github.com/PaParaZz1/PointNet"},{"title":"yevgm/deepadv3d","url":"https://github.com/yevgm/deepadv3d"},{"title":"witignite/Frustum-PointNet","url":"https://github.com/witignite/Frustum-PointNet"},{"title":"m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks","url":"https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks"},{"title":"alpemek/ais3d","url":"https://github.com/alpemek/ais3d"},{"title":"yanxp/PointNet","url":"https://github.com/yanxp/PointNet"},{"title":"ftdlyc/pointnet_pytorch","url":"https://github.com/ftdlyc/pointnet_pytorch"},{"title":"Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification","url":"https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification"},{"title":"Phimos/Paddle-PointNet","url":"https://github.com/Phimos/Paddle-PointNet"},{"title":"zgx0534/pointnet_win","url":"https://github.com/zgx0534/pointnet_win"},{"title":"timothylimyl/PointNet-Pytorch","url":"https://github.com/timothylimyl/PointNet-Pytorch"},{"title":"minhncedutw/pointnet1_keras","url":"https://github.com/minhncedutw/pointnet1_keras"},{"title":"Jagyan/ghost_radarpoint_detection","url":"https://github.com/Jagyan/ghost_radarpoint_detection"},{"title":"brendanmallery9/entropic-barycenters","url":"https://github.com/brendanmallery9/entropic-barycenters"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet"},{"title":"xiuyu0000/vision","url":"https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py"},{"title":"hnmspirit/pointnet1","url":"https://github.com/hnmspirit/pointnet1"},{"title":"yangyucheng000/pointnet","url":"https://github.com/yangyucheng000/pointnet"},{"title":"eth212/Qmind_ModelNet10_PointNet","url":"https://github.com/eth212/Qmind_ModelNet10_PointNet"},{"title":"smfirmin/DeepLearningFinalProject","url":"https://github.com/smfirmin/DeepLearningFinalProject"},{"title":"ECE685-FinalProject/3D-object-recognition","url":"https://github.com/ECE685-FinalProject/3D-object-recognition"},{"title":"KhusDM/PointNetTree","url":"https://github.com/KhusDM/PointNetTree"},{"title":"Lw510107/pointnet-2018.6.27-","url":"https://github.com/Lw510107/pointnet-2018.6.27-"},{"title":"xurui1217/pointnet.pytorch-master","url":"https://github.com/xurui1217/pointnet.pytorch-master"},{"title":"hnVfly/pointnet.mxnet","url":"https://github.com/hnVfly/pointnet.mxnet"},{"title":"LebronGG/PointNet","url":"https://github.com/LebronGG/PointNet"},{"title":"GOD-GOD-Autonomous-Vehicle/self-pointnet","url":"https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet"},{"title":"AlfredoZermini/PointNet","url":"https://github.com/AlfredoZermini/PointNet"},{"title":"wonderland-dsg/pointnet-grid","url":"https://github.com/wonderland-dsg/pointnet-grid"},{"title":"aviros/roatationPointnet","url":"https://github.com/aviros/roatationPointnet"},{"title":"THHHomas/mls","url":"https://github.com/THHHomas/mls"},{"title":"liuch37/pointnet","url":"https://github.com/liuch37/pointnet"},{"title":"yanx27/Pointnet","url":"https://github.com/yanx27/Pointnet"},{"title":"Taeuk-Jang/pointcompletion","url":"https://github.com/Taeuk-Jang/pointcompletion"},{"title":"ytng001/sensemaking","url":"https://github.com/ytng001/sensemaking"},{"title":"nddoshi/pointnet","url":"https://github.com/nddoshi/pointnet"},{"title":"SY-Xuan/pointnet_paddle","url":"https://github.com/SY-Xuan/pointnet_paddle"},{"title":"Cmeijiang/PointNet_MS","url":"https://github.com/Cmeijiang/PointNet_MS"},{"title":"AndreiMoraru123/PointNet","url":"https://github.com/AndreiMoraru123/PointNet"},{"title":"mengxingshifen1218/pointnet.pytorch","url":"https://github.com/mengxingshifen1218/pointnet.pytorch"},{"title":"Moon-Xuezha/PointNet","url":"https://github.com/Moon-Xuezha/PointNet"},{"title":"ajertec/PointNetKeras","url":"https://github.com/ajertec/PointNetKeras"},{"title":"2023-MindSpore-1/ms-code-35","url":"https://github.com/2023-MindSpore-1/ms-code-35"},{"title":"LONG-9621/Extract_Point_3D","url":"https://github.com/LONG-9621/Extract_Point_3D"},{"title":"Mind23-2/MindCode-63","url":"https://github.com/Mind23-2/MindCode-63"},{"title":"merazlab/3D_Deep_Learning_Link","url":"https://github.com/merazlab/3D_Deep_Learning_Link"},{"title":"2023-MindSpore-1/ms-code-7","url":"https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet"},{"title":"KaidongLi/tf-3d-alpha","url":"https://github.com/KaidongLi/tf-3d-alpha"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"prasadsawant5/PointNet","url":"https://github.com/prasadsawant5/PointNet"},{"title":"SonuDileep/3-D-Object-Detection-using-PointNet","url":"https://github.com/SonuDileep/3-D-Object-Detection-using-PointNet"},{"title":"BPMJG/annotated_pointnet","url":"https://github.com/BPMJG/annotated_pointnet"},{"title":"debajyotid2/pointnet_pl","url":"https://github.com/debajyotid2/pointnet_pl"},{"title":"SBPL-Cruz/perch_pose_sampler","url":"https://github.com/SBPL-Cruz/perch_pose_sampler"},{"title":"2023-MindSpore-4/Code9","url":"https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet"},{"title":"wuryantoAji/POINTNET","url":"https://github.com/wuryantoAji/POINTNET"},{"title":"LONG-9621/PointNet","url":"https://github.com/LONG-9621/PointNet"},{"title":"bt77/pointnet","url":"https://github.com/bt77/pointnet"},{"title":"MindSpore-paper-code-3/code9","url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2"},{"title":"KiranAkadas/My_Pointnet_v2","url":"https://github.com/KiranAkadas/My_Pointnet_v2"},{"title":"YiruS/pointnet_adversarial","url":"https://github.com/YiruS/pointnet_adversarial"},{"title":"Harut0726/my-pointnet-tensorflow","url":"https://github.com/Harut0726/my-pointnet-tensorflow"},{"title":"AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation","url":"https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation"},{"title":"hz-ants/pointnet.pytorch","url":"https://github.com/hz-ants/pointnet.pytorch"},{"title":"VaanHUANG/CSCI5210HW1","url":"https://github.com/VaanHUANG/CSCI5210HW1"},{"title":"MindSpore-paper-code-3/code9","url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet"},{"title":"ShadowShadowWong/update-pointnet-work","url":"https://github.com/ShadowShadowWong/update-pointnet-work"},{"title":"Yang2446/pointnet","url":"https://github.com/Yang2446/pointnet"},{"title":"lingzhang1/pointnet_pytorch","url":"https://github.com/lingzhang1/pointnet_pytorch"},{"title":"dwtstore/sfm1","url":"https://github.com/dwtstore/sfm1"},{"title":"freddieee/pn_6d_single","url":"https://github.com/freddieee/pn_6d_single"},{"title":"ahmed-anas/thesis-pointnet","url":"https://github.com/ahmed-anas/thesis-pointnet"},{"title":"coconutzs/PointNet_zs","url":"https://github.com/coconutzs/PointNet_zs"},{"title":"CheesyB/cpointnet","url":"https://github.com/CheesyB/cpointnet"},{"title":"lingzhang1/pointnet_tensorflow","url":"https://github.com/lingzhang1/pointnet_tensorflow"},{"title":"aviros/pointnet_totations","url":"https://github.com/aviros/pointnet_totations"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146179,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Subvolume","metrics":{"Overall Accuracy":"89.2"},"paper_url":"http://arxiv.org/abs/1604.03265v2","paper_title":"Volumetric and Multi-View CNNs for Object Classification on 3D Data","paper_date":"2016-04-12","code_links":[{"title":"charlesq34/3dcnn.torch","url":"https://github.com/charlesq34/3dcnn.torch"},{"title":"LONG-9621/3DCNN","url":"https://github.com/LONG-9621/3DCNN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146180,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"ECC","metrics":{"Mean Accuracy":"83.2","Overall Accuracy":"87.4"},"paper_url":"http://arxiv.org/abs/1704.02901v3","paper_title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","paper_date":"2017-04-10","code_links":[{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"mys007/ecc","url":"https://github.com/mys007/ecc"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146181,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"VRN (multiple views)","metrics":{"Mean Accuracy":"91.33"},"paper_url":"http://arxiv.org/abs/1608.04236v2","paper_title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","paper_date":"2016-08-15","code_links":[{"title":"ajbrock/Generative-and-Discriminative-Voxel-Modeling","url":"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146182,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"VRN (single view)","metrics":{"Mean Accuracy":"88.98"},"paper_url":"http://arxiv.org/abs/1608.04236v2","paper_title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","paper_date":"2016-08-15","code_links":[{"title":"ajbrock/Generative-and-Discriminative-Voxel-Modeling","url":"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146183,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"3DShapeNets","metrics":{"Mean Accuracy":"77.3"},"paper_url":"http://arxiv.org/abs/1406.5670v3","paper_title":"3D ShapeNets: A Deep Representation for Volumetric Shapes","paper_date":"2014-06-22","code_links":[{"title":"zhirongw/3DShapeNets","url":"https://github.com/zhirongw/3DShapeNets"},{"title":"hamidreza-dastmalchi/3dd-tta","url":"https://github.com/hamidreza-dastmalchi/3dd-tta"},{"title":"AliBahri94/SVWA_TTA","url":"https://github.com/AliBahri94/SVWA_TTA"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146184,"task":"3D Point Cloud Classification","parent_task":"3D Point Cloud Reconstruction","dataset":"ModelNet40","model_name":"Perceiver","metrics":{"Mean Accuracy":"14.3"},"paper_url":"https://arxiv.org/abs/2103.03206v2","paper_title":"Perceiver: General Perception with Iterative Attention","paper_date":"2021-03-04","code_links":[{"title":"deepmind/deepmind-research","url":"https://github.com/deepmind/deepmind-research/tree/master/perceiver"},{"title":"towhee-io/towhee","url":"https://github.com/towhee-io/towhee"},{"title":"lucidrains/perceiver-pytorch","url":"https://github.com/lucidrains/perceiver-pytorch"},{"title":"krasserm/perceiver-io","url":"https://github.com/krasserm/perceiver-io"},{"title":"Rishit-dagli/Perceiver","url":"https://github.com/Rishit-dagli/Perceiver"},{"title":"louislva/deepmind-perceiver","url":"https://github.com/louislva/deepmind-perceiver"},{"title":"Xianchao-Wu/perceiver-pytorch","url":"https://github.com/Xianchao-Wu/perceiver-pytorch"},{"title":"fac2003/perceiver-multi-modality-pytorch","url":"https://github.com/fac2003/perceiver-multi-modality-pytorch"},{"title":"sooheon/perceiver-jax","url":"https://github.com/sooheon/perceiver-jax"},{"title":"clementpoiret/Perceiver_MNIST","url":"https://github.com/clementpoiret/Perceiver_MNIST"},{"title":"yangyucheng000/University","url":"https://github.com/yangyucheng000/University/tree/main/model-1/perceiver"},{"title":"kietngt00/hmdb51-recognition","url":"https://github.com/kietngt00/hmdb51-recognition"}],"metrics_order":"[\"Overall Accuracy\", \"Mean Accuracy\", \"Number of params\", \"FLOPs\", \"Mean class accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]}]}