{"task":"3D Object Classification","dataset":"ModelNet40","metric_names":["Classification Accuracy"],"rows":[{"id":83204,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"Ours","metrics":{"Classification Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2304.14124v1","paper_title":"Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation","paper_date":"2023-04-27","code_links":[{"title":"jiamang/ibt","url":"https://github.com/jiamang/ibt"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83205,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83206,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83207,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"O-CNN(6)","metrics":{"Classification Accuracy":"89.9"},"paper_url":"http://arxiv.org/abs/1712.01537v1","paper_title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","paper_date":"2017-12-05","code_links":[{"title":"Microsoft/O-CNN","url":"https://github.com/Microsoft/O-CNN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83208,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"3D-PointCapsNet","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1812.10775v2","paper_title":"3D Point Capsule Networks","paper_date":"2018-12-27","code_links":[{"title":"yongheng1991/3D-point-capsule-networks","url":"https://github.com/yongheng1991/3D-point-capsule-networks"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83209,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"Spherical Kernel","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1909.09287v2","paper_title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","paper_date":"2019-09-20","code_links":[{"title":"hlei-ziyan/SPH3D-GCN","url":"https://github.com/hlei-ziyan/SPH3D-GCN"},{"title":"zb12138/sph3d","url":"https://github.com/zb12138/sph3d"},{"title":"zb12138/sph3dR","url":"https://github.com/zb12138/sph3dR"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83210,"task":"3D Object Classification","parent_task":"3D","dataset":"ModelNet40","model_name":"ECC (12 votes)","metrics":{"Classification Accuracy":"83.2"},"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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83629,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"Ours","metrics":{"Classification Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2304.14124v1","paper_title":"Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation","paper_date":"2023-04-27","code_links":[{"title":"jiamang/ibt","url":"https://github.com/jiamang/ibt"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83630,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":83631,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83632,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"O-CNN(6)","metrics":{"Classification Accuracy":"89.9"},"paper_url":"http://arxiv.org/abs/1712.01537v1","paper_title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","paper_date":"2017-12-05","code_links":[{"title":"Microsoft/O-CNN","url":"https://github.com/Microsoft/O-CNN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83633,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"3D-PointCapsNet","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1812.10775v2","paper_title":"3D Point Capsule Networks","paper_date":"2018-12-27","code_links":[{"title":"yongheng1991/3D-point-capsule-networks","url":"https://github.com/yongheng1991/3D-point-capsule-networks"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83634,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"Spherical Kernel","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1909.09287v2","paper_title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","paper_date":"2019-09-20","code_links":[{"title":"hlei-ziyan/SPH3D-GCN","url":"https://github.com/hlei-ziyan/SPH3D-GCN"},{"title":"zb12138/sph3d","url":"https://github.com/zb12138/sph3d"},{"title":"zb12138/sph3dR","url":"https://github.com/zb12138/sph3dR"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":83635,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"ECC (12 votes)","metrics":{"Classification Accuracy":"83.2"},"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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":89196,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Ours","metrics":{"Classification Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2304.14124v1","paper_title":"Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation","paper_date":"2023-04-27","code_links":[{"title":"jiamang/ibt","url":"https://github.com/jiamang/ibt"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":89197,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":89198,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":89199,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"O-CNN(6)","metrics":{"Classification Accuracy":"89.9"},"paper_url":"http://arxiv.org/abs/1712.01537v1","paper_title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","paper_date":"2017-12-05","code_links":[{"title":"Microsoft/O-CNN","url":"https://github.com/Microsoft/O-CNN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":89200,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"3D-PointCapsNet","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1812.10775v2","paper_title":"3D Point Capsule Networks","paper_date":"2018-12-27","code_links":[{"title":"yongheng1991/3D-point-capsule-networks","url":"https://github.com/yongheng1991/3D-point-capsule-networks"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":89201,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"Spherical Kernel","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1909.09287v2","paper_title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","paper_date":"2019-09-20","code_links":[{"title":"hlei-ziyan/SPH3D-GCN","url":"https://github.com/hlei-ziyan/SPH3D-GCN"},{"title":"zb12138/sph3d","url":"https://github.com/zb12138/sph3d"},{"title":"zb12138/sph3dR","url":"https://github.com/zb12138/sph3dR"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":89202,"task":"3D Object Classification","parent_task":null,"dataset":"ModelNet40","model_name":"ECC (12 votes)","metrics":{"Classification Accuracy":"83.2"},"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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106110,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"Ours","metrics":{"Classification Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2304.14124v1","paper_title":"Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation","paper_date":"2023-04-27","code_links":[{"title":"jiamang/ibt","url":"https://github.com/jiamang/ibt"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106111,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":106112,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106113,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"O-CNN(6)","metrics":{"Classification Accuracy":"89.9"},"paper_url":"http://arxiv.org/abs/1712.01537v1","paper_title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","paper_date":"2017-12-05","code_links":[{"title":"Microsoft/O-CNN","url":"https://github.com/Microsoft/O-CNN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106114,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"3D-PointCapsNet","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1812.10775v2","paper_title":"3D Point Capsule Networks","paper_date":"2018-12-27","code_links":[{"title":"yongheng1991/3D-point-capsule-networks","url":"https://github.com/yongheng1991/3D-point-capsule-networks"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106115,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"Spherical Kernel","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1909.09287v2","paper_title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","paper_date":"2019-09-20","code_links":[{"title":"hlei-ziyan/SPH3D-GCN","url":"https://github.com/hlei-ziyan/SPH3D-GCN"},{"title":"zb12138/sph3d","url":"https://github.com/zb12138/sph3d"},{"title":"zb12138/sph3dR","url":"https://github.com/zb12138/sph3dR"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":106116,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"ECC (12 votes)","metrics":{"Classification Accuracy":"83.2"},"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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":115300,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"Ours","metrics":{"Classification Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2304.14124v1","paper_title":"Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation","paper_date":"2023-04-27","code_links":[{"title":"jiamang/ibt","url":"https://github.com/jiamang/ibt"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":115301,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":115302,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":115303,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"O-CNN(6)","metrics":{"Classification Accuracy":"89.9"},"paper_url":"http://arxiv.org/abs/1712.01537v1","paper_title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","paper_date":"2017-12-05","code_links":[{"title":"Microsoft/O-CNN","url":"https://github.com/Microsoft/O-CNN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":115304,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"3D-PointCapsNet","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1812.10775v2","paper_title":"3D Point Capsule Networks","paper_date":"2018-12-27","code_links":[{"title":"yongheng1991/3D-point-capsule-networks","url":"https://github.com/yongheng1991/3D-point-capsule-networks"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":115305,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"Spherical Kernel","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1909.09287v2","paper_title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","paper_date":"2019-09-20","code_links":[{"title":"hlei-ziyan/SPH3D-GCN","url":"https://github.com/hlei-ziyan/SPH3D-GCN"},{"title":"zb12138/sph3d","url":"https://github.com/zb12138/sph3d"},{"title":"zb12138/sph3dR","url":"https://github.com/zb12138/sph3dR"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":115306,"task":"3D Object Classification","parent_task":"3D Classification","dataset":"ModelNet40","model_name":"ECC (12 votes)","metrics":{"Classification Accuracy":"83.2"},"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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146208,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"Ours","metrics":{"Classification Accuracy":"93.6"},"paper_url":"https://arxiv.org/abs/2304.14124v1","paper_title":"Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation","paper_date":"2023-04-27","code_links":[{"title":"jiamang/ibt","url":"https://github.com/jiamang/ibt"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146209,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"G3DNet-18 MLP, Fine-Tuned, Vote","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":146210,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"CrossMoCo","metrics":{"Classification 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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146211,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"O-CNN(6)","metrics":{"Classification Accuracy":"89.9"},"paper_url":"http://arxiv.org/abs/1712.01537v1","paper_title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","paper_date":"2017-12-05","code_links":[{"title":"Microsoft/O-CNN","url":"https://github.com/Microsoft/O-CNN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146212,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"3D-PointCapsNet","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1812.10775v2","paper_title":"3D Point Capsule Networks","paper_date":"2018-12-27","code_links":[{"title":"yongheng1991/3D-point-capsule-networks","url":"https://github.com/yongheng1991/3D-point-capsule-networks"},{"title":"CPUFronz/CapsVoxGAN","url":"https://github.com/CPUFronz/CapsVoxGAN"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146213,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"Spherical Kernel","metrics":{"Classification Accuracy":"89.3"},"paper_url":"https://arxiv.org/abs/1909.09287v2","paper_title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","paper_date":"2019-09-20","code_links":[{"title":"hlei-ziyan/SPH3D-GCN","url":"https://github.com/hlei-ziyan/SPH3D-GCN"},{"title":"zb12138/sph3d","url":"https://github.com/zb12138/sph3d"},{"title":"zb12138/sph3dR","url":"https://github.com/zb12138/sph3dR"}],"metrics_order":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":146214,"task":"3D Object Classification","parent_task":"3D Point Cloud Classification","dataset":"ModelNet40","model_name":"ECC (12 votes)","metrics":{"Classification Accuracy":"83.2"},"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":"[\"Classification Accuracy\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]}]}