ICCV-2021-point-cloud-analysis

ICCV 2021 papers and code focus on point cloud analysis

• Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis classification

  • [Code]

• Score-Based Point Cloud Denoising Denoising

  • [Code]

• ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation Segmentation

• HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration registration

  • [Code]

• Learning with Noisy Labels for Robust Point Cloud Segmentation segmentation oral

  • [Code]

• Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation segmentation Domain Adaptation

  • [Code]

• Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion Unsupervised learning

  • [Code]

• Group-Free 3D Object Detection via Transformers 3D Object Detection

• Hierarchical Aggregation for 3D Instance Segmentation segmentation

  • [Code]

• 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds. 3D Visual Grounding

• Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation. segmentation

• TempNet: Online Semantic Segmentation on Large-scale Point Cloud Series. segmentation

• Robustness Certification for Point Cloud Models. robustness

  • [Code]

• Shape Self-Correction for Unsupervised Point Cloud Understanding. Unsupervised learning

• Pyramid Point Cloud Transformer for Large-Scale Place Recognition. Place_Recognition

  • [Code]

• Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds. Domain Adaptation

  • [Code]

• Differentiable Convolution Search for Point Cloud Processing. NAS

• Learning Inner-group Relations on Point Clouds. classification

  • [Code]

• Point-Based Modeling of Human Clothing.

  • [Code]

• Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks. classification

  • [Code]