CVPR2020点群系リスト

• [CVPR] RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [tensorflow] [seg.]
• [CVPR] Learning multiview 3D point cloud registration. [code] [reg.]
• [CVPR] PF-Net: Point Fractal Network for 3D Point Cloud Completion. [pytorch] [oth.]
• [CVPR] ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes. [det.]
• [CVPR] Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. [pytorch] [seg.]
• [CVPR] AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss. [seg.]
• [CVPR] SA-SSD: Structure Aware Single-Stage 3D Object Detection from Point Cloud. [pytorch] [det.]
• [CVPR] PointAugment: an Auto-Augmentation Framework for Point Cloud Classification. [code] [classification.]
• [CVPR] Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [tensorflow][det.]
• [CVPR] Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds. [seg.]
• [CVPR] Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds. [pytorch][oth.]
• [CVPR] PointGMM: a Neural GMM Network for Point Clouds. [code][cls.]
• [CVPR] RPM-Net: Robust Point Matching using Learned Features. [code] [seg.]
• [CVPR] Unsupervised Learning of Intrinsic Structural Representation Points. [pytorch][oth.]
• [CVPR] PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. [pytorch] [seg.]
• [CVPR] 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. [seg.]
• [CVPR] DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes. [det.]
• [CVPR] OccuSeg: Occupancy-aware 3D Instance Segmentation. [seg.]
• [CVPR] MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps. [oth.]
• [CVPR] Learning to Segment 3D Point Clouds in 2D Image Space. [seg]
• [CVPR] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. [cls]
• [CVPR] PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. [cls.]
• [CVPR] Physically Realizable Adversarial Examples for LiDAR Object Detection. [det.]
• [CVPR] HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. [det]
• [CVPR] LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. [code][det.]
• [CVPR] PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. [seg.]
• [CVPR] DualSDF: Semantic Shape Manipulation using a Two-Level Representation. [code][seg]
• [CVPR] Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. [pytorch][det.]
• [CVPR] End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection. [code] [det.]
• [CVPR] Cascaded Refinement Network for Point Cloud Completion. [code][completion]
• [CVPR] MLCVNet: Multi-Level Context VoteNet for 3D Object Detection. [code][det.]
• [CVPR] Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions. [oth.]
• [CVPR] Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking. [track.]
• [CVPR] StructEdit: Learning Structural Shape Variations. [project] [rec.]
• [CVPR] Connect-and-Slice: an hybrid approach for reconstructing 3D objects. [reconstruction.]
• [CVPR] SGAS: Sequential Greedy Architecture Search. [pytorch] ['cls.']
• [CVPR oral] Deep Global Registration. ['reg.']
• [CVPR] 3DSSD: Point-based 3D Single Stage Object Detector. [det]
• [CVPR] Going Deeper with Point Networks. [pytorch]['cls.']
• [CVPR] Connect-and-Slice: an hybrid approach for reconstructing 3D objects. [reconstruction]
• [CVPR] Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. [registration]
• [CVPR] From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks. [tensorflow]['image-to-point cloud.']
• [CVPR] PointPainting: Sequential Fusion for 3D Object Detection. [detection]
• [CVPR] xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. [Segmentation]