https://github.com/cuge1995/cvpr-2020-point-cloud-analysis

CVPR 2020 papers focusing on point cloud analysis
https://github.com/cuge1995/cvpr-2020-point-cloud-analysis

computer-vision cvpr cvpr2020 deep-learning point-cloud

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CVPR 2020 papers focusing on point cloud analysis

• Host: GitHub
• URL: https://github.com/cuge1995/cvpr-2020-point-cloud-analysis
• Owner: cuge1995
• Created: 2020-06-11T08:35:51.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-01-13T12:35:16.000Z (over 4 years ago)
• Last Synced: 2025-01-12T22:33:01.536Z (3 months ago)
• Topics: computer-vision, cvpr, cvpr2020, deep-learning, point-cloud
• Homepage:
• Size: 126 KB
• Stars: 47
• Watchers: 4
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CVPR-2020-point-cloud-analysis

CVPR 2020 papers focusing on point cloud analysis

- [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://arxiv.org/abs/2003.03164)

  - [[Code](https://github.com/XuyangBai/D3Feat)]

- [RPM-Net: Robust Point Matching using Learned Features](https://arxiv.org/abs/2003.13479)

  - [[Code](https://github.com/yewzijian/RPMNet)]

- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://arxiv.org/abs/2003.01060)

- [Cascaded Refinement Network for Point Cloud Completion](https://arxiv.org/abs/2004.03327)

  - [[Code](https://github.com/xiaogangw/cascaded-point-completion)]

  

- [PointAugment: an Auto-Augmentation Framework for Point Cloud Classification](https://arxiv.org/abs/2002.10876)

  - [[Code](https://github.com/liruihui/PointAugment/)]

- [Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Weakly_Supervised_Semantic_Point_Cloud_Segmentation_Towards_10x_Fewer_Labels_CVPR_2020_paper.pdf)  ` segmention ` 

  - [[Code](https://github.com/alex-xun-xu/WeakSupPointCloudSeg)]

- [Neural Implicit Embedding for Point Cloud Analysis.](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fujiwara_Neural_Implicit_Embedding_for_Point_Cloud_Analysis_CVPR_2020_paper.pdf)  ` segmention ` ` classification `

  - [[Code](https://github.com/kentfuji/NeuralEmbedding)]

- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression.](https://arxiv.org/abs/2005.07178)  ` compression ` ` oral `

- [PF-Net: Point Fractal Network for 3D Point Cloud Completion](https://arxiv.org/abs/2003.00410)   ` completion `

  - [[Code](https://github.com/zztianzz/PF-Net-Point-Fractal-Network)]

- [End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection](https://arxiv.org/abs/2004.03080)

  - [[Code](https://github.com/mileyan/pseudo-LiDAR_e2e/tree/master/PIXOR)]

- [Going Deeper with Point Networks](http://geometry.cs.ucl.ac.uk/projects/2020/deepleanpn/paper_docs/GoingDeeperWithPointNetworksLeKokkinosMitra.pdf) ` segmentation `

  - [[Code](https://github.com/erictuanle/GoingDeeperwPointNetworks)]

- [Learning multiview 3D point cloud registration](https://arxiv.org/abs/2001.05119)

- [Geometry and Learning Co-supported Normal Estimation for UnstructuredPoint Cloud.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Geometry_and_Learning_Co-Supported_Normal_Estimation_for_Unstructured_Point_Cloud_CVPR_2020_paper.pdf)  ` normal estimation ` 

- [LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of

Point Cloud-based Deep Networks.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_LG-GAN_Label_Guided_Adversarial_Network_for_Flexible_Targeted_Attack_of_CVPR_2020_paper.pdf)  ` attack ` 

  - [[Code](https://github.com/RyanHangZhou/LG-GAN)]

- [On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.pdf)  ` attack ` 

  - [[Code](https://github.com/skywalker6174/3d-isometry-robust)]

- [Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.pdf)  ` classification ` ` segmentation ` 

  - [[Code](https://github.com/j1a0m0e4sNTU/3dgcn)]

- [An Efficient PointLSTM for Point Clouds Based Gesture Recognition.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Min_An_Efficient_PointLSTM_for_Point_Clouds_Based_Gesture_Recognition_CVPR_2020_paper.pdf)  ` Gesture Recognition ` 

  - [[Code](https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch)]

- [Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image](https://arxiv.org/abs/2002.12212)

  

- [In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks](https://arxiv.org/pdf/1911.11924.pdf)

- [Unsupervised Learning of Intrinsic Structural Representation Points](https://arxiv.org/pdf/2003.01661.pdf)

  - [[Code](https://github.com/NolenChen/3DStructurePoints)]

- [LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention](https://arxiv.org/pdf/2004.01389.pdf)

  - [[Code](https://github.com/yinjunbo/3DVID)]

- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://arxiv.org/abs/1911.11236)  ` segmentation `

  - [[Code](https://github.com/QingyongHu/RandLA-Net)]

  

- [C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds](https://arxiv.org/abs/1912.07009)

- [High-dimensional Convolutional Networks for Geometric Pattern Recognition.](https://arxiv.org/abs/2005.08144)  ` registrition ` ` oral `

  - [[Code](https://github.com/chrischoy/HighDimConvNets)]

- [Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs](https://arxiv.org/abs/2003.00287)

- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://arxiv.org/pdf/2004.01658.pdf)

  - [[Code](https://github.com/Jia-Research-Lab/PointGroup)]

  

- [Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion](https://arxiv.org/abs/2003.01456)

  - [[Code](https://github.com/RaminHasibi/SA_Net)]

- [Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences](http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Feature-Metric_Registration_A_Fast_Semi-Supervised_Approach_for_Robust_Point_Cloud_CVPR_2020_paper.pdf)

  - [[Code](https://github.com/XiaoshuiHuang/fmr)]

  

- [From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks](http://openaccess.thecvf.com/content_CVPR_2020/papers/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.pdf)

  - [[Code](https://github.com/klnavaneet/ssl_3d_recon)]

- [Associate-3Ddet: Perceptual-to-Conceptual association for 3D Point Cloud Object Detection](http://openaccess.thecvf.com/content_CVPR_2020/papers/Du_Associate-3Ddet_Perceptual-to-Conceptual_Association_for_3D_Point_Cloud_Object_Detection_CVPR_2020_paper.pdf) ` detection `

  - [[Code](https://github.com/dleam/Associate-3Ddet)]

 

- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://arxiv.org/pdf/2003.01251.pdf)  ` detection `

  - [[Code](https://github.com/WeijingShi/Point-GNN)]

- [PointGMM: a Neural GMM Network for Point Clouds](https://arxiv.org/pdf/2003.13326.pdf)

  - [[Code](https://github.com/amirhertz/pointgmm)]

- [3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation](https://arxiv.org/pdf/2003.13867.pdf)

  - [[Code](https://github.com/francisengelmann/3D-MPA)]

- [PointPainting: Sequential Fusion for 3D Object Detection](https://arxiv.org/abs/1911.10150)  ` detection `

  - [[Code](https://github.com/rshilliday/painting)]

- [Point Cloud Completion by Skip-attention Network with Hierarchical Folding](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.pdf)  ` completion `

- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling.](https://arxiv.org/pdf/2003.00492.pdf)  ` classification ` ` segmentation `

  - [[Code](https://github.com/yanx27/PointASNL)]

- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation.](https://arxiv.org/pdf/2003.14032.pdf)  ` segmentation `

  - [[Code](https://github.com/edwardzhou130/PolarSeg)]

- [Grid-GCN for Fast and Scalable Point Cloud Learning.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Grid-GCN_for_Fast_and_Scalable_Point_Cloud_Learning_CVPR_2020_paper.pdf)  ` learning `

  - [[Code](https://github.com/Xharlie/Grid-GCN)]

- [End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_End-to-End_Learning_Local_Multi-View_Descriptors_for_3D_Point_Clouds_CVPR_2020_paper.pdf)  ` Registration `

  - [[Code](https://github.com/craigleili/3DLocalMultiViewDesc)]

- [On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.pdf)  ` attack `

  - [[Code](https://github.com/skywalker6174/3d-isometry-robust)]

- [SampleNet: Differentiable Point Cloud Sampling.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.pdf)  ` sampling `

  - [[Code](https://github.com/itailang/SampleNet)]

- [SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lei_SegGCN_Efficient_3D_Point_Cloud_Segmentation_With_Fuzzy_Spherical_Kernel_CVPR_2020_paper.pdf)  ` segmentation `

  - [[Code](https://github.com/hlei-ziyan/SegGCN)]

- [ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes.](https://arxiv.org/abs/2001.10692)  `Detection ` 

  - [[Code](https://github.com/facebookresearch/imvotenet)]

- [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds.](https://arxiv.org/abs/2005.13888)  ` Tracking `   `Oral`

  - [[Code](https://github.com/HaozheQi/P2B)]