3D Point Cloud Linear Classification
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Benchmarks
ModelNet40
ScanObjectNN
Most implemented
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
SO-Net: Self-Organizing Network for Point Cloud Analysis
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Papers
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
Manual annotation of large-scale point cloud datasets is laborious due to their irregular structure. While cross-modal contrastive learning methods such as CrossPoint and CrossNet have progressed in utilizing multimodal …
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationContrastive Learning+1Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
Recent advancements in self-supervised learning in the point cloud domain have demonstrated significant potential. However, these methods often suffer from drawbacks, including lengthy pre-training time, the necessity of…
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationClassification+2ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. ShapeLLM is built upon…
3D geometry3D Object Captioning3D Point Cloud Classification3D Point Cloud Linear Classification+13CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
The point cloud is a 3D geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field of vision tasks. However, most…
3D Object Classification3D Point Cloud Classification3D Point Cloud Linear ClassificationContrastive Learning+3Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Mainstream 3D representation learning approaches are built upon contrastive or generative modeling pretext tasks, where great improvements in performance on various downstream tasks have been achieved. However, we find t…
3D Point Cloud Classification3D Point Cloud Linear ClassificationDecoderFew-Shot 3D Point Cloud Classification+2Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Pre-training by numerous image data has become de-facto for robust 2D representations. In contrast, due to the expensive data acquisition and annotation, a paucity of large-scale 3D datasets severely hinders the learning…
3D Point Cloud Classification3D Point Cloud Linear ClassificationDecoderFew-Shot 3D Point Cloud Classification