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awesome-point-cloud-analysis-2023

A list of papers and datasets about point cloud analysis (processing) since 2017. Update every day!
https://github.com/NUAAXQ/awesome-point-cloud-analysis-2023

Last synced: 2 days ago
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  • 2020

    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACM MM
    • [WACV - Modal Data. [__`seg.`__ __`aut.`__]
    • [3DV - UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection. [__`Detection`__]
    • [IROS - time Deep Multitask Network for LiDAR-based 3D Object Detection and Scene Flow Estimation. [__`Detection, Flow`__]
    • [WACV
    • [WACV
    • [IROS - to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences.[__` Registration `__]
    • [AAAI - Point-Cloud-Completion)] [__`oth.`__]
    • [AAAI
    • [AAAI - Node for Point Cloud Feature Modeling. [__`seg.`__ __`cls.`__]
    • [AAAI - Invariant Network. [__`seg.`__ __`cls.`__]
    • [AAAI - Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. [__`oth`__]
    • [AAAI
    • [AAAI - Aware Adaptive Zooming Neural Network for 3D Object Detection. [__`det.`__]
    • [AAAI - Oriented Convolution Neural Network for Point Cloud Analysis. [__`cls.`__]
    • [CVPR - Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [[tensorflow](https://github.com/QingyongHu/RandLA-Net)] [__`seg.`__] :fire:
    • [CVPR
    • [CVPR - Net: Point Fractal Network for 3D Point Cloud Completion. [[pytorch](https://github.com/zztianzz/PF-Net-Point-Fractal-Network.git)] [__`oth.`__]
    • [CVPR
    • [CVPR - Segmentation with Group Consistency Loss. [__`seg.`__]
    • [CVPR - SSD: Structure Aware Single-Stage 3D Object Detection from Point Cloud. [[pytorch](https://github.com/skyhehe123/SA-SSD)] [__`det.`__] :fire:
    • [CVPR - Augmentation Framework for Point Cloud Classification. [[code](https://github.com/liruihui/PointAugment/)] [__`classification.`__]
    • [CVPR - GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [[tensorflow](https://github.com/WeijingShi/Point-GNN)][__`det.`__] :fire:
    • [CVPR - Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds. [__`seg.`__]
    • [CVPR - Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds. [[pytorch](https://github.com/raoyongming/PointGLR)][__`oth.`__]
    • [CVPR
    • [CVPR - Net: Robust Point Matching using Learned Features. [[code](https://github.com/yewzijian/RPMNet)] [__`seg.`__]
    • [CVPR
    • [CVPR
    • [CVPR - MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. [__`seg.`__]
    • [CVPR
    • [CVPR - aware 3D Instance Segmentation. [__`seg.`__]
    • [CVPR
    • [CVPR - VISLab/Learning-to-Segment-3D-Point-Clouds-in-2D-Image-Space)] [__`seg`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR - based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. [[code](https://github.com/yinjunbo/3DVID)][__`det.`__]
    • [CVPR - Set Point Grouping for 3D Instance Segmentation. [__`seg.`__]
    • [CVPR - Level Representation. [[code](https://github.com/zekunhao1995/DualSDF)][__`seg`__]
    • [CVPR - CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. [[pytorch](https://github.com/zju3dv/disprcnn)][__`det.`__]
    • [CVPR - to-End Pseudo-LiDAR for Image-Based 3D Object Detection. [[code](https://github.com/mileyan/pseudo-LiDAR_e2e)] [__`det.`__]
    • [CVPR - point-completion)][__`completion`__]
    • [CVPR - Level Context VoteNet for 3D Object Detection. [[code](https://github.com/NUAAXQ/MLCVNet)][__`det.`__]
    • [CVPR
    • [CVPR - Temporal Optimization for Stereo 3D Object Tracking. [__`track.`__]
    • [CVPR
    • [CVPR
    • [CVPR oral
    • [CVPR - based 3D Single Stage Object Detector. [__`det`__]
    • [CVPR
    • [CVPR - and-Slice: an hybrid approach for reconstructing 3D objects. [__`reconstruction`__]
    • [CVPR - metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. [__`registration`__]
    • [CVPR - supervised Shape and Pose Networks. [[tensorflow](https://github.com/val-iisc/ssl_3d_recon)][__'image-to-point cloud.'__]
    • [CVPR
    • [CVPR - Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. [__`Segmentation`__]
    • [CVPR
    • [CVPR oral - Structured Entropy Model for LiDAR Compression. [__`Compression`__]
    • [CVPR
    • [CVPR oral - dimensional Convolutional Networks for Geometric Pattern Recognition. [[code](https://github.com/chrischoy/HighDimConvNets)][__`Recognition`__]
    • [CVPR oral - to-Box Network for 3D Object Tracking in Point Clouds. [[pytorch](https://github.com/HaozheQi/P2B)][__`Tracking`__]
    • [CVPR - 3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. [__`detection`__]
    • [CVPR - D Scans. [__`Completion`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR - to-End 3D Point Cloud Instance Segmentation Without Detection. [__`Segmentation`__]
    • [CVPR - Sampling for Point Cloud Classification. [__`Classification`__]
    • [CVPR - Supported Normal Estimation for Unstructured Point Cloud. [__`Normal`__]
    • [CVPR
    • [CVPR
    • [CVPR - GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks. [__`Attack`__]
    • [CVPR
    • [CVPR
    • [CVPR - GCN for Fast and Scalable Point Cloud Learning. [__`other`__]
    • [CVPR
    • [CVPR - attention Network with Hierarchical Folding. [__`Completion`__]
    • [BMVC - Net: Attention and Structure Aware Point Cloud Sequence Segmentation. [__`Segmentation`__]
    • [CVPR - to-End Learning Local Multi-View Descriptors for 3D Point Clouds. [__`Description`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPRW
    • [ECCV - CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. [[code](https://github.com/rasd3/3D-CVF)][__`Detection`__]
    • [ECCV
    • [ECCV
    • [ECCV
    • [ECCV - Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation. [__`Flow`__]
    • [ECCV
    • [ECCV - Efficient Learning on Point Clouds using Approximate Convex Decompositions. [[code](https://github.com/matheusgadelha/PointCloudLearningACD)][__`Learning`__]
    • [NIPS - Invariant Local-to-Global Representation Learning for 3D Point Cloud. [__`Representation`__]
    • [ECCV - Net: A Geometry-centric Network for 3D Point Cloud Upsampling. [__`Upsampling`__]
    • [ECCV
    • [ECCV - Convolutional Representations for Point Cloud Denoising. [__`Denoising`__]
    • [ECCV - Preserved-Point-Cloud-Completion-via-SFA)][__`Completion`__]
    • [ECCV
    • [NIPS
    • [ECCV
    • [ECCV - deformnet)][__`Pose`__]
    • [ECCV
    • [ECCV - based Object Detection for Autonomous Driving. [[tensorflow](https://github.com/WangYueFt/pillar-od)][__`Detection`__]
    • [ECCV - Scale 6DoF Relocalization. [[pytorch](https://vision.in.tum.de/research/vslam/dh3d)][__`Localization`__]
    • [ECCV - Extrinsic Ratio Guidance. [__`Meshing`__]
    • [ECCV
    • [NIPS
    • [ECCV - training for 3D Point Cloud Understanding. [__`Unsupervised,Understanding`__]
    • [ECCV
    • [ECCV - Deform: Deformable Fitting of CAD Models to 3D Scans. [__`Fitting`__]
    • [ECCV
    • [ECCV - Prediction for Joint Instance and Semantic Segmentation of Point Clouds. [__`Segmentation`__]
    • [ECCV - view Fusion for 3D Semantic Segmentation. [__`Segmentation`__]
    • [ECCV - Voxel Convolution. [__`Segmentation`__]
    • [ECCV - chen/PointMixup/)][__`Classification`__]
    • [ECCV
    • [ECCV - robust Intersection over Union for 3D Object Detection. [__`3D IOU`__]
    • [ECCV
    • [ECCV
    • [ECCV
    • [ECCV - supervised 3D Shape Completion in the Wild. [__`Completion`__]
    • [ECCV
    • [ECCV - Adaptive Convolution for Efficient Point-Cloud Segmentation. [[code](https://github.com/chenfengxu714/SqueezeSegV3)][__`seg.`__]
    • [ECCV
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv
    • [arXiv - View fusion for 3D Shape Recognition. [__`cls.`__]
    • [arXiv - Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection. [__`det.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - supervised Point Set Local Descriptors for Point Cloud Registration. [__`reg.`__]
    • [arXiv
    • [3DV
    • [3DV
    • [3DV - Convolutional Network to Estimate Inlier Points. [[pytorch](https://github.com/vinits5/masknet)][__` Registration`__]
    • [ACM MM - Qin/Weakly-Supervised-3D-Object-Detection)][__`Detection`__]
    • [ACM MM
    • [ICML - Resilient Point Cloud Processing. [__`Classification.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - 1/PointHop-PointHop2_Spark)][__`cls.`__]
    • [arXiv - Invariant Framework for Deep Point Cloud Analysis. [__`oth.`__]
    • [arXiv - Local Part-Aware Point Cloud Denoising. [__`oth.`__]
    • [arXiv - Flow: Conditional Generative Flow Models for Images and 3D Point Clouds. [__`oth.`__]
    • [arXiv
    • [arXiv - time 3D object proposal generation and classification under limited processing resources. [__`det.`__]
    • [arXiv - view Semantic Learning Network for Point Cloud Based 3D Object Detection. [__`seg.`__]
    • [arXiv
    • [ICRA - LiDAR Point Cloud Interpolation. [__`completion.`__]
    • [ICRA
    • [ICRA - agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds. [__`det.`__]
    • [TPAMI
    • [ICLR - gan-pub )] [__`com.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [CG
    • [ISPRS
    • [GMP - Net: Learning Discriminative Features on Point Clouds by EncodingLocal Region Contexts. [__`cls.`__]
    • [SPM - preserving Normal Estimation for Point Cloud Filtering. [__`normal.`__]
    • [Master Thesis
    • [arXiv - 3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways. [[code](https://github.com/WeikaiTan/Toronto-3D)][__`seg.`__]
    • [arXiv - Supervised Learning for Domain Adaptation on Point-Clouds.[[code](https://github.com/IdanAchituve/DefRec_and_PCM)] [__`oth.`__]
    • [arXiv
    • [arXiv - D Scans.[[code](https://github.com/skanti/SceneCAD)] [__`oth.`__]
    • [arXiv
    • [arXiv - Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. [__`cls.`__]
    • [arXiv - Efficient Learning on Point Clouds using Approximate Convex Decompositions. [[pytorch](https://github.com/matheusgadelha/PointCloudLearningACD)][__`oth.`__]
    • [arXiv - Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds. [__`det.`__]
    • [arXiv
    • [arXiv
    • [arXiv - Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification. [__`oth.`__]
    • [arXiv - Aware 3D Model Embedding and Retrieval. [__`oth.`__]
    • [arXiv - Based Point Clouds. [__`seg.`__]
    • [arXiv - domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation. [__`seg.`__]
    • [arXiv
    • [arXiv - Net: IoU Guided 3D Object Detector for Point Clouds. [__`det.`__]
    • [arXiv
    • [arXiv - based Lidar Super-resolution for Ground Vehicles. [[tensorflow](https://github.com/RobustFieldAutonomyLab/lidar_super_resolution)][__`oth.`__]
    • [arXiv
    • [arXiv
    • [arXiv - 3D-2D Cycle. [[code](https://github.com/qq456cvb/SemanticTransfer)][__`oth.`__]
    • [arXiv - attention convolutional network for segmentation of point clouds. [[code](https://github.com/RayleighChen/point-attention)] [__`seg.`__]
    • [arXiv - CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. [__`det.`__]
    • [arXiv - Structured Point Clouds of Wild Scenes. [__`seg.`__]
    • [arXiv
    • [arXiv - Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling. [__`sampling.`__]
    • [arXiv
    • [arXiv - Inspired 3D Moving Object Detection Based on Point Clouds. [__`detection.`__]
    • [arXiv - attention Network with Hierarchical Folding. [__`Completion.`__]
    • [arXiv - Resolution Network for Point Cloud Classification and Segmentation.[[code](https://github.com/ShiQiu0419/DRNet)] [__`segmentation.`__]
    • [arXiv - Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data. [__`segmentation.`__]
    • [arXiv
    • [arXiv - Aware Adversarial 3D Point Clouds. [__`Generation.`__]
    • [arXiv
    • [arXiv - Conv: Permutable Anisotropic Convolutional Networks for Learning on Point Clouds. [__`Classification.`__]
    • [arXiv
    • [arXiv - 3D Correspondences for One-shot Partial-to-partial Registration. [__`Registration.`__]
    • [arXiv - based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion. [__`Completion.`__]
    • [arXiv - Based Methods. [__`other.`__]
    • [arXiv
    • [arXiv - shot Object Detection. [__`Detection.`__]
    • [arXiv - - 3D Detection and Domain Adaptation. [__`Detection.`__]
    • [arXiv - - 2D and 3D Tracking. [__`Detection.`__]
    • [arXiv - NET: Parametric Inference of Point Cloud Edges. [__`Edge Detection.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - Aware Data Augmentation for 3D Object Detection in Point Cloud. [__`Detection.`__]
    • [arXiv - scene LiDAR Semantic Segmentation. [[code](https://github.com/xinge008/Cylinder3D)][__`Segmentation.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - Net: Completion of 3D Vehicles from MLS Point Clouds. [__`Completion.`__]
    • [arXiv - point-based Segmentation: A New Paradigm for LiDAR Point Cloud Segmentation. [__`Segmentation.`__]
    • [arXiv - wise Attention Module for 6D Object Pose Estimation. [__`Pose.`__]
    • [arXiv - Sampling for Neural Point Cloud Consolidation. [__`Consolidation.`__]
    • [arXiv
    • [arXiv - Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation. [__`Segmentation`__]
    • [arXiv - Labs/3D-Point-Cloud-Learning)] [__`Survey`__]
    • [arXiv
    • [arXiv - Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. [__`Detection`__]
    • [arXiv - 3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector. [__`Registration`__]
    • [arXiv - based methods for analyzing orchard tree structure using noisy point cloud data. [__` `__]
    • [arXiv - Training by Completing Point Clouds[[torch]( https://github.com/hansen7/OcCo)]. [__`Completion`__]
    • [arXiv
    • [arXiv - Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds. [__`Grasping`__]
    • [arXiv
    • [arXiv
    • [arXiv - Points3D: A Modular Multi-Task Framework for Reproducible Deep Learning on 3D Point Clouds.[[torch]( https://github.com/nicolas-chaulet/torch-points3d)][__`Framework`__]
    • [arXiv - Point-Cloud-Completion)][__` Completion `__]
    • [arXiv - supervised Cascaded Refinement Network for Point Cloud Completion. [__` Completion`__]
    • [arXiv - Induced feaTure Extraction for Point Cloud Registration. [__` Registration`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - LiDAR 3D Object Detection for Autonomous Driving.[[code](http://143.89.78.112:5000/sharing/90BpyDIuq)][__`Detection `__]
    • [arXiv - Registration: Learning to Learn Registration of 3D Point Clouds. [__`Registration `__]
    • [arXiv - View Adaptive Fusion Network for 3D Object Detection. [__`Detection`__]
    • [arXiv - Abstraction Refinement Network for 3D Point Cloud Analysis. [[code](https://github.com/ruc98/MARNet)][__`Analysis`__]
    • [arXiv
    • [arXiv - Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. [__`Detection`__]
    • [arXiv
    • [arXiv - Invariant Point Cloud Analysis. [__`Rotation`__]
    • [arXiv
    • [arXiv
    • [arXiv - Features Guidance Network for partial-to-partial point cloud registration. [__`registration`__]
    • [arXiv - based Point Cloud Prediction Network. [__`Motion`__]
    • [arXiv
    • [arXiv - Arbitrary Upsampling over 3D Point Clouds. [__`Upsampling`__]
    • [arXiv - overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds.[__`Detection`__]
    • [arXiv - Feature Density for 3D Semantic Segmentation of Point Clouds.[__` Segmentation`__]
    • [arXiv
    • [arXiv - based lossless compression of 3D point cloud geometry.[__`Compression`__]
    • [arXiv
    • [arXiv - image representation for 3D point cloud generation.[__`Generation`__]
    • [arXiv - attention?[__`Networks`__]
    • [arXiv
    • [arXiv - based Multi-View Fusion Network for LiDAR Semantic Segmentation.[__`Segmentation`__]
    • [arXiv - Net: A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition.[__`Registration`__]
    • [arXiv - Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization.[__`Upsampling`__]
    • [arXiv
    • [IROS - Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation. [__`Segmentation `__]
    • [IROS - feature-enhanced 3D Scene Understanding Network with Dual Spatial-aware Discriminative Loss. [__`Segmentation `__]
    • [IROS - local Neural Network for Point Cloud Semantic Segmentation. [__`Segmentation`__]
    • [IROS - to-Real and Partial Observability in Visual Navigation. [__`Navigation`__]
    • [IROS - Object Tracking in Point Clouds. [__`Tracking`__]
    • [IROS
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds. [[pytorch](https://github.com/pumpkinnan/BAN)] [__`seg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [arXiv
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Aware PointRCNN for 3D Objection Detection in Point Clouds.[__`Detection`__]
    • [arXiv - based Rotation and Translation-Invariant Transformation for Point Clouds Recognition.[[code]( https://github.com/shenqildr/ScorNet )] [__`Recognition `__]
    • [arxiv
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [arXiv
    • [arXiv
    • [ICRA
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - class Object Detection from Point Clouds. [[code](https://github.com/xinge008/SSN)][__`det.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv
    • [arXiv
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ECCV - Scale 6DoF Relocalization. [[pytorch](https://vision.in.tum.de/research/vslam/dh3d)][__`Localization`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - supervised Point Set Local Descriptors for Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ECCV
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arxiv
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [CVPR
    • [CVPR - attention Network with Hierarchical Folding. [__`Completion`__]
    • [CVPR
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [CVPR - Set Point Grouping for 3D Instance Segmentation. [__`seg.`__]
    • [ECCV
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection. [__`det.`__]
    • [arXiv
    • [arXiv
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [ECCV - Voxel Convolution. [__`Segmentation`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Aware PointRCNN for 3D Objection Detection in Point Clouds.[__`Detection`__]
    • [arXiv - Labs/3D-Point-Cloud-Learning)] [__`Survey`__]
    • [arXiv - LiDAR 3D Object Detection for Autonomous Driving.[[code](http://143.89.78.112:5000/sharing/90BpyDIuq)][__`Detection `__]
    • [ACM MM - Qin/Weakly-Supervised-3D-Object-Detection)][__`Detection`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [ECCV
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Efficient Learning on Point Clouds using Approximate Convex Decompositions. [[pytorch](https://github.com/matheusgadelha/PointCloudLearningACD)][__`oth.`__]
    • [arXiv - based methods for analyzing orchard tree structure using noisy point cloud data. [__` `__]
    • [arXiv
    • [arXiv - Induced feaTure Extraction for Point Cloud Registration. [__` Registration`__]
    • [IROS - to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences.[__` Registration `__]
    • [ACM MM
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. [__`cls.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [AAAI
    • [CVPR - Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [[tensorflow](https://github.com/QingyongHu/RandLA-Net)] [__`seg.`__] :fire:
    • [CVPR - Net: Point Fractal Network for 3D Point Cloud Completion. [[pytorch](https://github.com/zztianzz/PF-Net-Point-Fractal-Network.git)] [__`oth.`__]
    • [CVPR - Augmentation Framework for Point Cloud Classification. [[code](https://github.com/liruihui/PointAugment/)] [__`classification.`__]
    • [CVPR
    • [arXiv
    • [ECCV
    • [ECCV - training for 3D Point Cloud Understanding. [__`Unsupervised,Understanding`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation. [__`Segmentation`__]
    • [WACV - Modal Data. [__`seg.`__ __`aut.`__]
    • [ACIIDS - supervised Representation Learning for 3D Point Clouds. [__`oth.`__]
    • [[ECCV - Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [__`reg.`__]
    • [arXiv - Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds. [[pytorch](https://github.com/pumpkinnan/BAN)] [__`seg.`__]
    • [arXiv - D Scans.[[code](https://github.com/skanti/SceneCAD)] [__`oth.`__]
    • [CVPR - Segmentation with Group Consistency Loss. [__`seg.`__]
    • [CVPR - Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds. [__`seg.`__]
    • [CVPR - aware 3D Instance Segmentation. [__`seg.`__]

  • 2022

    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [CVPR
    • [arXiv - Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds. [__`Segmentation.`__]
    • [arXiv - Specific Smoothing for Point Cloud Models. [__`Classification.`__]
    • [arXiv - C)] [__`Classification.`__]
    • [arXiv - Aware Attention for 3D Point Cloud Classification. [[code](https://github.com/YangFengSEU/CEDR)] [__`Classification.`__]
    • [arXiv - aware Geometric Encoding Network for Point Cloud Registration. [[code](https://github.com/zhulf0804/NgeNet)] [__`Registration.`__]
    • [arXiv - ren/ModelNet-C)] [__`Classification.`__]
    • [arXiv
    • [arXiv - based Large-scale Contexts Model for Point Cloud Compression. [[code](https://github.com/zb12138/OctAttention)] [__`Compression.`__]
    • [WACV - representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers. [[code](https://github.com/rayguan97/M3DETR)] [__`Detection.`__]
    • [ICLR - xu/pointMLP-pytorch)] [__`Classification`__]
    • [ICLR
    • [AAAI - Augmented Set Abstraction for Point-based 3D Object Detection. [[code](https://github.com/blakechen97/SASA)] [__`Detection`__]
    • [CVPR
    • [CVPR - NeRF: Point-based Neural Radiance Fields. [[code](https://github.com/Xharlie/pointnerf)] [__`Reconstruction`__]
    • [CVPR
    • [CVPR - Centric Paradigm for 3D Single Object Tracking in Point Clouds. [[code](https://github.com/Ghostish/Open3DSOT)] [__`Tracking`__]
    • [CVPR
    • [CVPR - Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding. [[code](https://github.com/MohamedAfham/CrossPoint)] [__`Understanding`__]
    • [CVPR - Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation. [[code](https://github.com/robinwang1/ART-Point)] [__`Rotation`__]
    • [CVPR - invariant 3D Adversarial Point Clouds. [[code](https://github.com/shikiw/SI-Adv)] [__`Adversarial`__]
    • [CVPR - supervised 3D Object Detection with Shape-guided Label Enhancement. [[code](https://github.com/wyf-ACCEPT/BackToReality)] [__`Detection`__]
    • [CVPR
    • [CVPR - Aware Voxels for LiDAR 3D Object Detection. [[code](https://github.com/TRAILab/PDV)] [__`Detection`__]
    • [CVPR
    • [CVPR - Camera Deep Fusion for Multi-Modal 3D Object Detection. [[code](https://github.com/tensorflow/lingvo/tree/master/lingvo)] [__`Detection`__]
    • [CVPR - Supervised LiDAR Semantic Segmentation. [[code](https://github.com/ouenal/scribblekitti)] [__`Segmentation`__]
    • [CVPR - BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling. [[code](https://github.com/lulutang0608/Point-BERT)] [__`BERT`__]
    • [CVPR - based Detectorsfor 3D LiDAR Point Clouds. [[code](https://github.com/yifanzhang713/IA-SSD)] [__`Detection`__]
    • [CVPR - to-Set Approach to 3D Object Detection from Point Clouds. [[code](https://github.com/skyhehe123/VoxSeT)] [__`Detection`__]
    • [CVPR - Camera Fusion for 3D Object Detection with Transformers. [[code](https://github.com/XuyangBai/TransFusion/)] [__`Detection`__]
    • [CVPR - Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment. [[code](https://github.com/ZENGYIMING-EAMON/IDEA-Net)] [__`Interpolation`__]
    • [CVPR - Normalized 3D Perception. [[code](https://github.com/hustvl/AziNorm)] [__`Symmetry`__]
    • [CVPR
    • [CVPR
    • [CVPR - to-end Point Cloud Correspondences with Transformers. [[code](https://github.com/yewzijian/RegTR)] [__`Registration`__]
    • [CVPR - research/Stratified-Transformer)] [__`Segmentation`__]
    • [CVPR - OrientedMP)] [__`Classification`__]
    • [CVPR - PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration. [[code](https://github.com/ZhiChen902/SC2-PCR)] [__`Registration`__]
    • [CVPR
    • [CVPR
    • [CVPR - to-Point-Cloud Cross-Modal Localization. [[code](https://text2pos.github.io/)] [__`Localization`__]
    • [CVPR
    • [CVPR - level Space-time Surfaces. [[code](https://github.com/jx-zhong-for-academic-purpose/Kinet)] [__`Flow`__]
    • [CVPR - Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. [__`Detection`__]
    • [CVPR - to-Real Scene Flow Estimation for Point Clouds. [[code](https://github.com/leolyj/DCA-SRSFE)] [__`Flow`__]
    • [CVPR - View Transformer for 3D Visual Grounding. [[code](https://github.com/sega-hsj/MVT-3DVG)] [__`Grounding`__]
    • [CVPR
    • [CVPR - based Grouping for 3D Object Detection. [[code](https://github.com/Haiyang-W/RBGNet)] [__`Detection`__]
    • [CVPR
    • [CVPR oral - SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection. [[code](https://github.com/fjhzhixi/3D-SPS)] [__`Visual Grounding`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR - TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation. [[code](https://www.nec-labs.com/~mas/MM-TTA/)] [__`Segmentation`__]
    • [CVPR - preserving Deep Point Cloud Compression. [[code](https://github.com/yunhe20/D-PCC)] [__`Compression`__]
    • [CVPR
    • [CVPR oral
    • [CVPR - Affine Transformation for Point Cloud Segmentation. [[code](https://github.com/wangzy22/SemAffiNet)] [__`Segmentation`__]
    • [CVPR
    • [CVPR
    • [CVPR - instance Point Cloud Registration by Efficient Correspondence Clustering. [[code](https://github.com/SJTU-ViSYS/multi-instant-reg)] [__`Registration`__]
    • [CVPR
    • [CVPR - vislab.github.io/)] [__`Attack`__]
    • [CVPR
    • [CVPR - training with Natural 3D Structures. [[code](https://ryosuke-yamada.github.io/PointCloud-FractalDataBase/)] [__`Pre-training`__]
    • [CVPR - based Paradigm for Point Cloud Understanding. [__`Understanding`__]
    • [CVPR - Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior. [__`Flow`__]
    • [CVPR
    • [CVPR - VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection. [[code](https://crashd-cars.github.io/)] [__`Detection`__]
    • [CVPRW - Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences [__`oth.`__]
    • [CVPR - Derived Transformer for Weakly Supervised Point Cloud Segmentation. [[code](https://github.com/jimmy15923/wspss_mil_transformer)] [__`Segmentation`__]
    • [CVPR
    • [CVPR - Scale Processing in Point Cloud Segmentation. [[code](https://github.com/ginobilinie/kp_pyramid)] [__`Segmentation`__]
    • [CVPR
    • [CVPR - Reference Point Cloud Quality Assessment via Domain Adaptation. [[code](https://github.com/Qi-Yangsjtu/IT-PCQA)] [__`Assessment`__]
    • [CVPR
    • [CVPR - Cloud-Color-Constancy)] [__`Color`__]
    • [CVPR
    • [CVPR - Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation. [[code](https://github.com/xnowbzhao/sapcu)] [__`Upsampling`__]
    • [CVPR - Range Marker-Less 3D Human Motion Capture With LiDAR Point Clouds. [__`Motion`__]
    • [CVPR - Aware Implicits. [[code](https://github.com/Jhonve/ImplicitPCDA)] [__`Adaptation`__]
    • [CVPR
    • [CVPR - Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints. [__`Completion`__]
    • [CVPR - Supervised 3D Object Detection from Point Cloud. [__`Detection`__]
    • [CVPR - Net: Learning Upright Orientation for 3D Point Cloud. [__`Orientation`__]
    • [CVPR - Surface Priors. [[code](https://github.com/mabaorui/OnSurfacePrior)] [__`Reconstruction`__]
    • [CVPR - Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization. [__`Segmentation`__]
    • [CVPR - Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation with Reliable Voted Pseudo Labels. [__`Adaptation`__]
    • [CVPR
    • [CVPR - Site/)] [__`Neural Representations`__]
    • [ECCV - world semantic segmentation for Lidar Point Clouds. [[code](https://github.com/Jun-CEN/Open_world_3D_semantic_segmentation)] [__`Segmentation`__]
    • [ECCV
    • [ECCV
    • [ECCV - inf.mpg.de/box2mask/)] [__`Segmentation`__]
    • [ECCV - Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds. [[code](https://github.com/ttchengab/MetaSampler)] [__`Sampling`__]
    • [ECCV - PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation. [[code](https://github.com/cwc1260/BiFlow)] [__`Scene Flow`__]
    • [ECCV - Supervised Learning on Point Clouds. [[code](https://github.com/haotian-liu/MaskPoint)] [__`Self-Supervised Learning`__]
    • [ECCV
    • [ECCV
    • [ECCV - Guided Auxiliary Training Improves 3D Object Detector. [[code](https://github.com/FabienCode/LG3D)] [__`Detection`__]
    • [ECCV - eth/PCAccumulation)] [__`Scene`__]
    • [ECCV
    • [ECCV - Term 4D Point Cloud Video Understanding. [__`Video Understanding`__]
    • [ECCV - supervised Line Segmentation and Description for LiDAR Point Cloud. [[code](https://github.com/zxrzju/SuperLine3D.git)] [__`Segmentation`__]
    • [ECCV - supervised LiDAR De-snowing through Reconstruction Difficulty. [__`De-snowing`__]
    • [ECCV - CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. [[code](https://github.com/Nightmare-n/GraphRCNN)] [__`Detection`__]
    • [ECCV - Robust Point Cloud Encoder with Relaxed K-D Trees. [[code](https://github.com/immortalCO/PointTree)] [__`Encoder`__]
    • [ECCV
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - 4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds. [[code](https://github.com/hkust-vgd/RFNet-4D?utm_source=catalyzex.com)] [__`Flow`__]
    • [ECCV - Efficient Semantic Segmentation for LiDAR Point Clouds. [__`Segmentation`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - level Object Pose Refinement. [[code](https://github.com/THU-DA-6D-Pose-Group/CATRE)] [__`Registration`__]
    • [ECCV - Domain Point Cloud Completion via Occlusion Factor Manipulation. [[code](https://github.com/azuki-miho/OptDE)] [__`Completion`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV
    • [ECCV
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [CVPR
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [CVPR
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [CVPR
    • [CVPR - to-Point-Cloud Cross-Modal Localization. [[code](https://text2pos.github.io/)] [__`Localization`__]
    • [CVPR - based Grouping for 3D Object Detection. [[code](https://github.com/Haiyang-W/RBGNet)] [__`Detection`__]
    • [CVPR
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]
    • [CVPR - PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration. [[code](https://github.com/ZhiChen902/SC2-PCR)] [__`Registration`__]
    • [ECCV - research/3DVision/tree/main/PointCompletion/FBNet)] [__`Completion`__]
    • [ECCV - Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [[code](https://github.com/pigtigger/FH-Net)] [__`Flow`__]
    • [ECCV - Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [[code](https://github.com/Dangzheng/MatchNorm)] [__`Registration`__]
    • [ECCV - 3D-Keypoints-with-Mutual-Recosntruction)] [__`Reconstruction`__]

  • 2023

    • [WAD
    • [IQmulus & TerraMobilita Contest
    • [WAD
    • [CVPR - PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions. [[code](https://github.com/yunhe20/Grad-PU)] [__`Upsampling`__]
    • [CVPR - Insensitive Unsupervised Domain Adaption on 3D Object Detection. [[code](https://github.com/WoodwindHu/DTS)] [__`Detection`__]
    • [CVPR - based Object Detection. [[code](https://github.com/ZZY816/COM)] [__`Detection`__]
    • [CVPR - trained Models via Image-to-Point Masked Autoencoders. [[code](https://github.com/ZrrSkywalker/I2P-MAE)] [__`Representations`__]
    • [CVPR - Parametric Networks for 3D Point Cloud Analysis. [[code](https://github.com/ZrrSkywalker/Point-NN)] [__`Representations`__]
    • [CVPR - Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers. [[code](https://github.com/ZechuanLi/AShapeFormer)] [__`Detection`__]
    • [CVPR
    • [CVPR - Temporal Neural Field for 3D Point Cloud Multi-Frame Non-Linear Interpolation. [[code](https://github.com/ispc-lab/NeuralPCI)] [__`Interpolation`__]
    • [CVPR - Supervised Learning for Point Clouds in the Wild. [[code](https://github.com/YanhaoWu/STSSL)] [__`Understanding`__]
    • [CVPR - Modulated Point Cloud Rendering With Easy Editing. [[code](https://github.com/yizhangphd/FreqPCR)] [__`Nerf`__]
    • [CVPR - ORNet: Self-Ensembling Orientation-Aware Network for Unsupervised Point Cloud Shape Correspondence. [__`Correspondence`__]
    • [CVPR
    • [CVPR - Guided Sequential Fusion for Efficient 3D Object Detection From Point Cloud Sequences. [[code](https://github.com/skyhehe123/MSF)] [__`Detection`__]
    • [ICCV - based Decision Boundary Generation for Hard-label 3D Point Cloud Attack. [__`Attack`__]
    • [ICCV - Object Tracking. [[code](https://github.com/dsx0511/3DMOTFormer)] [__`Tracking`__]
    • [ICCV - RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds. [[code](https://github.com/ZhangCYG/U-RED)] [__`Retrieval`__]
    • [ICCV - MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds. [[code](https://github.com/minhaolee/2D3DMATR)] [__`Registration`__]
    • [ICCV - Wise
    • [ICCV - Camera 3D Object Detection Transformers. [[code](https://github.com/drilistbox/3DPPE)] [__`Detection`__]
    • [ICCV - 3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds. [[code](https://github.com/Liz66666/GPA3D)] [__`Detection`__]
    • [ICCV - Supervised Point Cloud Completion from Single Partial Clouds. [[code](https://github.com/CuiRuikai/Partial2Complete)] [__`Completion`__]
    • [ICCV - supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data. [[code](https://github.com/IIT-PAVIS/SC3K)] [__`Keypoints`__]
    • [ShapeNet
    • [PartNet
    • [S3DIS - Scale 3D Indoor Spaces Dataset. [__`seg.`__]
    • [ScanNet - annotated 3D Reconstructions of Indoor Scenes. [__`cls.`__ __`seg.`__]
    • [Stanford 3D
    • [UWA Dataset
    • [Princeton Shape Benchmark
    • [ASL Datasets Repository(ETH)
    • [Large-Scale Point Cloud Classification Benchmark(ETH)
    • [Robotic 3D Scan Repository - dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada.
    • [Oakland 3-D Point Cloud Dataset - D point cloud laser data collected from a moving platform in a urban environment.
    • [Robotic 3D Scan Repository
    • [Ford Campus Vision and Lidar Data Set - 250 pickup truck.
    • [The Stanford Track Collection - 64E S2 LIDAR.
    • [WAD
    • [nuScenes - scale autonomous driving dataset.
    • [PreSIL - wise segmentation (point clouds), ground point labels (point clouds), and detailed annotations for all vehicles and people. [[paper](https://arxiv.org/abs/1905.00160)] [__`det.`__ __`aut.`__]
    • [PedX - resolution (12MP) stereo images and LiDAR data along with providing 2D and 3D labels of pedestrians. [[ICRA 2019 paper](https://arxiv.org/abs/1809.03605)] [__`pos.`__ __`aut.`__]
    • [Argoverse BY ARGO AI - driving vehicles how to understand the world around them.[[CVPR 2019 paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.html)][__`tra.`__ __`aut.`__]
    • [SynthCity
    • [Lyft Level 5 - labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map. [__`det.`__ __`seg.`__ __`aut.`__]
    • [Oxford Robotcar
    • [DALES - scale Aerial LiDAR Data Set for Semantic Segmentation. [__`seg.`__]
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [CVPR
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [Ford Campus Vision and Lidar Data Set - 250 pickup truck.
    • [WAD
    • [WAD
    • [WAD
    • [PartNet
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [WAD
    • [ICCV - Object Tracking. [[code](https://github.com/dsx0511/3DMOTFormer)] [__`Tracking`__]
    • [ICCV - RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds. [[code](https://github.com/ZhangCYG/U-RED)] [__`Retrieval`__]
    • [WAD
    • [CVPR - Parametric Networks for 3D Point Cloud Analysis. [[code](https://github.com/ZrrSkywalker/Point-NN)] [__`Representations`__]
    • [WAD

  • 2021

    • [CVPR
    • [CVPR - to-End Learning of Dense Correspondence for 3D Point Clouds. [[code](https://github.com/ZENGYIMING-EAMON/CorrNet3D)][__`Correspondence`__]
    • [CVPR - Point-Flow: Self-Supervised Scene Flow Estimation From Point Clouds With Optimal Transport and Random Walk. [__`Flow`__]
    • [CVPR - Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding. [__`Flow`__]
    • [ICCV - invariant Deep Point Cloud Analysis. [__`Analysis`__]
    • [CVPR
    • [CVPR - Net: A Self-Attention and Orientation Encoding Network for Point Cloud Based Place Recognition. [[code](https://github.com/Yan-Xia/SOE-Net)][__`Recognition`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - Level Supervision for 3D Instance and Semantic Segmentation. [__`Segmentation.`__]
    • [arXiv - RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection. [__`Detection.`__]
    • [arXiv
    • [arXiv
    • [CVPR - S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network. [__`Segmentation`__]
    • [CVPR
    • [CVPR - inversion)][__`Completion`__]
    • [CVPR - SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. [[code](https://github.com/Vegeta2020/SE-SSD)][__`Detection`__]
    • [CVPR oral - Temporal Modeling in Point Cloud Videos. [[pytorch](https://github.com/hehefan/P4Transformer)][__`Transformer`__]
    • [ICCV
    • [ICCV - Invariant Point Cloud Representations via Sorted Gram Matrix. [__`Representations`__]
    • [ICCV
    • [CVPR - Net: An Octree based Framework for Point Cloud Compression. [__`Compression`__]
    • [CVPR
    • [CVPR
    • [CVPR - PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation. [__`Segmentation`__]
    • [CVPR
    • [CVPR - CNN: An Efficient and Universal 3D Object Detector. [[code](https://github.com/tusimple/LiDAR_RCNN)][__`Detection`__]
    • [CVPR - Point Representation for Single-stage 3D Object Detection. [__`Detection`__]
    • [CVPR - Temporal Attention for Scene Point Clouds. [__`Flow`__]
    • [CVPR - to-Point Cloud Registration via Deep Classification. [[code](https://github.com/lijx10/DeepI2P)][__`Registration`__]
    • [CVPR - tracing Representative Points for Voting-based 3D Object Detection in Point Clouds. [[pytorch](https://github.com/cheng052/BRNet)][__`Detection`__]
    • [arXiv - Efficient 3D Scene Understanding with Contrastive Scene Contexts. [__`Understanding.`__]
    • [arXiv - Scale Place Recognition. [[tensorflow](https://github.com/fpthink/EPC-Net)] [__`Place Recognition.`__]
    • [arXiv - Attention Based Context-Aware 3D Object Detection. [[pytorch](https://github.com/AutoVision-cloud/SA-Det3D)] [__`Detection.`__]
    • [arXiv - RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [[pytorch](https://github.com/open-mmlab/OpenPCDet)] [__`Detection.`__]
    • [arXiv - Oriented PointNet for 3D Object Detection in Point Clouds. [__`Detection.`__]
    • [arXiv - level Feature Learning from Point Clouds. [__`Learning.`__]
    • [arXiv - set Distances for Learning Representations of 3D Point Clouds. [__`Distances.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [ICLR - Temporal Convolution on Point Cloud Sequences. [__`Sequences`__]
    • [ICLR
    • [TOG
    • [CoRL - supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks. [[code](github.com/PRBonn/point-cloud-prediction)] [__`Prediction.`__]
    • [CVPR - point-cloud)][__`Generation`__]
    • [CVPR
    • [CVPR - based Point Generator with Adversarial Rendering for Point Cloud Completion. [__`Completion`__]
    • [CVPR oral - Body Segmentation and Motion Estimation via 3D Scan Synchronization. [[code](https://github.com/huangjh-pub/multibody-sync)][__`Synchronization`__]
    • [CVPR
    • [CVPR
    • [CVPR - Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. [[code](https://github.com/QingyongHu/SensatUrban)][__`Segmentation`__]
    • [CVPR
    • [CVPR - training for Unsupervised Domain Adaptation on 3D Object Detection. [[code](https://github.com/CVMI-Lab/ST3D)][__`Detection`__]
    • [CVPR
    • [CVPR - Oriented Reasoning for 3D Point-based Scene Graph Analysis. [__`Scene analysis`__]
    • [CVPR - Preserving are Line Clouds? Recovering Scene Details from 3D Lines. [[code](https://github.com/kunalchelani/Line2Point)][__`Scene recover`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR - GCN: Point Cloud Upsampling using Graph Convolutional Networks. [[code](https://github.com/guochengqian/PU-GCN)][__`Upsampling`__]
    • [CVPR - Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation.[[code](https://github.com/leofansq/SCF-Net)] [__`Segmentation`__]
    • [CVPR
    • [CVPR - Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models. [__`Self-Supervised`__]
    • [CVPR - Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction From Raw Point Clouds. [__`Self-Similarities`__]
    • [CVPR
    • [CVPR
    • [CVPR - Friendly Representations. [__`Autoencoder`__]
    • [CVPR - Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization. [[code](https://github.com/IRMVLab/PWCLONet)][__`Odometry`__]
    • [CVPR - RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds. [__`Flow`__]
    • [CVPR - Net: Category Guided Aggregation for Point Cloud Semantic Segmentation. [__`Segmentation`__]
    • [CVPR - PU)][__`Upsampling`__]
    • [CVPR - Net: Point Cloud Completion by Learning Multi-step Point Moving Paths. [[code](https://github.com/diviswen/PMP-Net)][__`Completion`__]
    • [CVPR - Guided Point Cloud Completion. [__`Completion`__]
    • [CVPR - shot 3D Point Cloud Semantic Segmentation. [[code](https://github.com/Na-Z/attMPTI)][__`Segmentation`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR - Lee/PointNetLK_Revisited)][__`Registration`__]
    • [CVPR - Up One-Stage 3D Object Detector With Integrated Multi-Level Features. [__`Detection`__]
    • [ICCV - Representation Iterative Learning Network for Point Cloud Segmentation. [__`Segmentation`__]
    • [ICCV
    • [ICCV - Based Point Cloud Denoising. [__`Denoising`__]
    • [ICCV - scale Outdoor LiDAR Point Cloud Registration. [[code](https://ispc-group.github.io/hregnet?utm_source=catalyzex.com)][__`Registration`__]
    • [ICCV oral
    • [ICCV - based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. [__`Segmentation`__]
    • [ICCV
    • [ICCV - master)][__`Analysis`__]
    • [ICCV - PCN: Point Completion Conditioned on Mask Emptiness. [__`Completion`__]
    • [ICCV - training from Synthetic Shapes and Randomized Layouts for 3D Object Detection. [__`Detection`__]
    • [ICCV - Lab-SCUT/SSTNet)][__`Segmentation`__]
    • [ICCV
    • [ICCV - Group Relations on Point Clouds. [__`Learning`__]
    • [ICCV
    • [ICCV - wise Transformer. [__`Detection`__]
    • [ICCV - temporal Self-Supervised Representation Learning for 3D Point Clouds. [__`Learning`__]
    • [ICCV - CNN: Towards Better Performance and Adaptability for 3D Object Detection. [__`Detection`__]
    • [ICCV
    • [ICCV
    • [ICCV - Transformer. [[code](https://github.com/AllenXiangX/SnowflakeNet)][__`Completion`__]
    • [ICCV
    • [ICCV - Free 3D Object Detection via Transformers. [[code](https://github.com/zeliu98/Group-Free-3D)][__`Detection`__]
    • [ICCV - Entropy Method for Unsupervised Point Cloud Registration. [__`Registration`__]
    • [ICCV oral - to-End Transformer Model for 3D Object Detection. [[code](https://github.com/facebookresearch/3detr)][__`Detection`__]
    • [ICCV
    • [ICCV
    • [ICCV - Level Voting Siamese Network for 3D Visual Tracking.[[code](https://github.com/CodeWZT/MLVSNet)] [__`Tracking`__]
    • [ICCV - Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection. [__`Detection`__]
    • [ICCV - scale Point Cloud Series. [__`Segmentation`__]
    • [ICCV - Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation. [__`Segmentation`__]
    • [ICCV - Supervised Point Cloud Semantic Segmentation. [__`Segmentation`__]
    • [ICCV - training via Occlusion Completion.[[code](https://github.com/hansen7/OcCo)] [__`training`__]
    • [ICCV - Transformer: Relation Modeling for Visual Grounding on Point Clouds. [__`Visual Grounding`__]
    • [ICCV - Scale Place Recognition.[[code](https://github.com/fpthink/PPT-Net)] [__`Recognition`__]
    • [ICCV - CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration.[[code](https://github.com/ChirikjianLab/LSG-CPD)] [__`Registration`__]
    • [ICCV
    • [ICCV
    • [ICCV
    • [ICCV - Aware Transformers.[[code](https://github.com/yuxumin/PoinTr)] [__`Completion`__]
    • [ICCV - Centric Omnidirectional Localization. [__`Localization`__]
    • [ICCV - EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling. [__`Upsampling`__]
    • [ICCV
    • [ICCV
    • [ICCV - Voxel Diffusion.[[code](https://alexzhou907.github.io/pvd)] [__`Completion`__]
    • [ICCV
    • [ICCV - to-Partial Point Cloud Registration.[[code](https://github.com/megvii-research/OMNet)] [__`Registration`__]
    • [ICCV
    • [ICCV - Aware Feature Enhancement for Single Object Tracking on Point Clouds.[[code](https://github.com/Ghostish/BAT)] [__`Tracking`__]
    • [ICCV
    • [ICCV
    • [ICCV
    • [ICCV
    • [ICCV
    • [ICCV - Attention Matrices for Rigid Registration of Point Clouds. [__`Registration`__]
    • [ICCV - Resolution Point Clouds.[[code](https://github.com/erictuanle/CPFN)] [__`Fitting`__]
    • [ICCV
    • [ICCV - Segmentation by Co-Contrastive Learning and Mutual Attention Sampling.[[code](https://github.com/jimmy15923/unsup_point_coseg)][__`Segmentation`__]
    • [ICCV
    • [ICCV - Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation.[__`Segmentation`__]
    • [ICCV
    • [ICCV - Correction for Unsupervised Point Cloud Understanding.[__`Understanding`__]
    • [ICCV
    • [ICCV - Level Contextual Referring. [[code](https://github.com/CurryYuan/InstanceRefer)] [__`Visual Grounding`__]
    • [ICCV - Encoder for Unsupervised Point Cloud Learning.[__`Encoder`__]
    • [CVPR - to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation [__`reg.`__]
    • [TPAMI - Rigid Point Set Registration With Downsampling and Gaussian Process Regression. [__`reg.`__]
    • [TPAMI - Based Metric and Efficient Global Optimization. [__`reg.`__]
    • [TPAMI - Aware Non-Rigid Point Cloud Registration. [__`reg.`__]
    • [NeurIPS - to-fine Correspondences for Robust PointCloud Registration. [__`reg.`__]
    • [NeurIPS
    • [CVPR - based 3D Object Detection and Tracking. [[pytorch](https://github.com/tianweiy/CenterPoint)][__`Detection`__]
    • [arXiv
    • [CVPR - Supervised 3D Object Detection. [[pytorch](https://github.com/thu17cyz/3DIoUMatch)][__`Detection`__]
    • [ICCV - Supervised Pretraining of 3D Features on any Point-Cloud. [[code](https://github.com/facebookresearch/DepthContrast)][__`Detection`__]
    • [CVPR - point-cloud)][__`Generation`__]

  • 2018

    • [ICRA - Cue Photometric Point Cloud Registration. [__`reg.`__]
    • [ICRA - Time SLAM for 3D Lidar-Based Online Mapping. [__`oth.`__]
    • [ICRA - LiDAR System. [__`oth.`__]
    • [ICRA
    • [ICRA - Sensor Fusion for 3D Mapping and Localization. [__`oth.`__]
    • [ICRA
    • [IROS
    • [IROS
    • [IROS - Photogrammetry with Random Patterns. [__`rec.`__ __`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Environment 3D LiDAR Localization. [__`oth.`__]
    • [IROS
    • [IROS - LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[[code](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM)] [__`pos.`__ __`oth.`__] :fire:
    • [IROS
    • [IROS
    • [IROS
    • [IROS
    • [IROS - Cost RGB-D Sensor. [[code](https://github.com/CTTCGeoLab/VI_ROS)][__`oth.`__]
    • [IROS - Time Amodal 3D Object Detection. [__`det.`__ __`pos.`__]
    • [IROS
    • [IROS
    • [IROS - Net)]3DmFV: Point Cloud Classification and segmentation for unstructured 3D point clouds. [__`cls.`__ ]
    • [IROS
    • [SENSORS
    • [ACCV - Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds). [[tensorflow](https://github.com/cgtuebingen/Flex-Convolution)] [__`seg.`__]
    • [3DV
    • [ICASSP - CNN for 3D Point Cloud Classification. [[tensorflow](https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification)] [__`cls.`__] :fire:
    • [ITSC
    • [arXiv - like Network Module for 3D Point Cloud Semantic Segmentation. [[tensorflow](https://github.com/MVIG-SJTU/pointSIFT)] [__`seg.`__] :fire:
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - Aware Surface Reconstruction for Point Clouds. [__`rec.`__]
    • [arXiv
    • [arXiv
    • [arXiv - based Object Detector for Point Cloud. [__`det.`__]
    • [arXiv - controllable Point Cloud Simplification on Graph. [__`oth.`__]
    • [arXiv
    • [arXiv - YOLO: Real-time 3D Object Detection on Point Clouds. [[pytorch](https://github.com/AI-liu/Complex-YOLO)] [__`det.`__ __`aut.`__] :fire:
    • [arxiv
    • [arXiv - column Point-CNN for Sketch Segmentation. [__`seg.`__]
    • [arXiv - Attention. [[project](https://liuziwei7.github.io/projects/PointGrow)] [__`oth.`__]
    • [arXiv - Cloud-GAN)] [__`oth.`__]
    • [AAAI - point-cloud-generation)] [__`rec.`__] :fire:
    • [AAAI
    • [ICML
    • [SIGGRAPH - NET: Bidirectional Point Displacement Net for Shape Transform. [[tensorflow](https://github.com/kangxue/P2P-NET)] [__`oth.`__]
    • [SIGGRAPH Asia - Uniformly Sampled Point Clouds. [[tensorflow](https://github.com/viscom-ulm/MCCNN)] [__`cls.`__ __`seg.`__ __`oth.`__]
    • [SIGGRAPH - view convolutional networks. [[project](https://people.cs.umass.edu/~hbhuang/local_mvcnn/index.html)] [__`seg.`__ __`oth.`__]
    • [MM - View for 3D Shape Recognition. [__`cls.`__ __`rel.`__]
    • [MM
    • [MM - based Layered Structure and Block-based Intra Prediction. [__`oth.`__]
    • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`det.`__ __`tra.`__ __`aut.`__]
    • [ICRA - View 3D Entangled Forest for Semantic Segmentation and Mapping. [__`seg.`__ __`oth.`__]
    • [ICRA - Time Road-Object Segmentation from 3D LiDAR Point Cloud. [[tensorflow](https://github.com/priyankanagaraj1494/Squeezseg)] [__`seg.`__ __`aut.`__]
    • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`det.`__]
    • [ICRA - Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [__`dep.`__ __`aut.`__]
    • [ICRA - Point Network for Classification of Deformed Building Element Point Clouds. [__`cls.`__]
    • [ICRA - Guided Geometry Extraction from Point Clouds. [__`oth.`__]
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA - Based Exploration for Autonomous 3D Modeling. [__`oth.`__ __`aut.`__]
    • [ICRA
    • [ICRA - Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [__`oth.`__]
    • [ICRA - Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [__`tra.`__]
    • [ICRA
    • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`det.`__ __`tra.`__ __`aut.`__]
    • [ICRA - View 3D Entangled Forest for Semantic Segmentation and Mapping. [__`seg.`__ __`oth.`__]
    • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`det.`__]
    • [ICRA - Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [__`dep.`__ __`aut.`__]
    • [ICRA - Point Network for Classification of Deformed Building Element Point Clouds. [__`cls.`__]
    • [ICRA - Guided Geometry Extraction from Point Clouds. [__`oth.`__]
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA - Cue Photometric Point Cloud Registration. [__`reg.`__]
    • [ICRA - Time SLAM for 3D Lidar-Based Online Mapping. [__`oth.`__]
    • [ICRA - LiDAR System. [__`oth.`__]
    • [ICRA - Based Exploration for Autonomous 3D Modeling. [__`oth.`__ __`aut.`__]
    • [ICRA
    • [ICRA
    • [ICRA - Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [__`oth.`__]
    • [ICRA - Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [__`tra.`__]
    • [ICRA - Sensor Fusion for 3D Mapping and Localization. [__`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Photogrammetry with Random Patterns. [__`rec.`__ __`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Environment 3D LiDAR Localization. [__`oth.`__]
    • [IROS
    • [IROS - LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[[code](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM)] [__`pos.`__ __`oth.`__] :fire:
    • [IROS
    • [IROS
    • [IROS
    • [IROS - Cost RGB-D Sensor. [[code](https://github.com/CTTCGeoLab/VI_ROS)][__`oth.`__]
    • [IROS - Time Amodal 3D Object Detection. [__`det.`__ __`pos.`__]
    • [IROS
    • [IROS
    • [IROS
    • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`det.`__ __`tra.`__ __`aut.`__]
    • [ICRA - View 3D Entangled Forest for Semantic Segmentation and Mapping. [__`seg.`__ __`oth.`__]
    • [ICRA - Time Road-Object Segmentation from 3D LiDAR Point Cloud. [[tensorflow](https://github.com/priyankanagaraj1494/Squeezseg)] [__`seg.`__ __`aut.`__]
    • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`det.`__]
    • [ICRA - Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [__`dep.`__ __`aut.`__]
    • [ICRA - Point Network for Classification of Deformed Building Element Point Clouds. [__`cls.`__]
    • [ICRA - Guided Geometry Extraction from Point Clouds. [__`oth.`__]
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA - Based Exploration for Autonomous 3D Modeling. [__`oth.`__ __`aut.`__]
    • [ICRA
    • [ICRA - Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [__`oth.`__]
    • [ICRA - Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [__`tra.`__]
    • [ICRA
    • [ICRA - Cue Photometric Point Cloud Registration. [__`reg.`__]
    • [ICRA - Time SLAM for 3D Lidar-Based Online Mapping. [__`oth.`__]
    • [ICRA - LiDAR System. [__`oth.`__]
    • [ICRA
    • [ICRA - Sensor Fusion for 3D Mapping and Localization. [__`oth.`__]
    • [ICRA
    • [IROS
    • [IROS
    • [IROS - Photogrammetry with Random Patterns. [__`rec.`__ __`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Environment 3D LiDAR Localization. [__`oth.`__]
    • [IROS
    • [IROS - LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[[code](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM)] [__`pos.`__ __`oth.`__] :fire:
    • [IROS
    • [IROS
    • [IROS
    • [IROS
    • [IROS - Cost RGB-D Sensor. [[code](https://github.com/CTTCGeoLab/VI_ROS)][__`oth.`__]
    • [IROS - Time Amodal 3D Object Detection. [__`det.`__ __`pos.`__]
    • [IROS
    • [IROS
    • [IROS
    • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`det.`__ __`tra.`__ __`aut.`__]
    • [ICRA - View 3D Entangled Forest for Semantic Segmentation and Mapping. [__`seg.`__ __`oth.`__]
    • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`det.`__]
    • [ICRA - Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [__`dep.`__ __`aut.`__]
    • [ICRA - Point Network for Classification of Deformed Building Element Point Clouds. [__`cls.`__]
    • [ICRA - Guided Geometry Extraction from Point Clouds. [__`oth.`__]
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA - Based Exploration for Autonomous 3D Modeling. [__`oth.`__ __`aut.`__]
    • [ICRA
    • [ICRA - Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [__`oth.`__]
    • [ICRA - Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [__`tra.`__]
    • [ICRA
    • [ICRA - Cue Photometric Point Cloud Registration. [__`reg.`__]
    • [ICRA - Time SLAM for 3D Lidar-Based Online Mapping. [__`oth.`__]
    • [ICRA - LiDAR System. [__`oth.`__]
    • [ICRA
    • [ICRA - Sensor Fusion for 3D Mapping and Localization. [__`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Photogrammetry with Random Patterns. [__`rec.`__ __`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Environment 3D LiDAR Localization. [__`oth.`__]
    • [IROS
    • [IROS - LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[[code](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM)] [__`pos.`__ __`oth.`__] :fire:
    • [IROS
    • [IROS
    • [IROS
    • [IROS - Cost RGB-D Sensor. [[code](https://github.com/CTTCGeoLab/VI_ROS)][__`oth.`__]
    • [IROS - Time Amodal 3D Object Detection. [__`det.`__ __`pos.`__]
    • [IROS
    • [IROS
    • [IROS
    • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`det.`__ __`tra.`__ __`aut.`__]
    • [ICRA - View 3D Entangled Forest for Semantic Segmentation and Mapping. [__`seg.`__ __`oth.`__]
    • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`det.`__]
    • [ICRA - Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [__`dep.`__ __`aut.`__]
    • [ICRA - Point Network for Classification of Deformed Building Element Point Clouds. [__`cls.`__]
    • [ICRA - Guided Geometry Extraction from Point Clouds. [__`oth.`__]
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA - Based Exploration for Autonomous 3D Modeling. [__`oth.`__ __`aut.`__]
    • [ICRA - Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [__`oth.`__]
    • [ICRA - Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [__`tra.`__]
    • [ICRA
    • [ICRA - Cue Photometric Point Cloud Registration. [__`reg.`__]
    • [ICRA - Time SLAM for 3D Lidar-Based Online Mapping. [__`oth.`__]
    • [ICRA - LiDAR System. [__`oth.`__]
    • [ICRA
    • [ICRA - Sensor Fusion for 3D Mapping and Localization. [__`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Photogrammetry with Random Patterns. [__`rec.`__ __`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Environment 3D LiDAR Localization. [__`oth.`__]
    • [IROS
    • [IROS - LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[[code](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM)] [__`pos.`__ __`oth.`__] :fire:
    • [IROS
    • [IROS
    • [IROS
    • [IROS - Cost RGB-D Sensor. [[code](https://github.com/CTTCGeoLab/VI_ROS)][__`oth.`__]
    • [IROS - Time Amodal 3D Object Detection. [__`det.`__ __`pos.`__]
    • [IROS
    • [IROS
    • [IROS
    • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`det.`__ __`tra.`__ __`aut.`__]
    • [ICRA - View 3D Entangled Forest for Semantic Segmentation and Mapping. [__`seg.`__ __`oth.`__]
    • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`det.`__]
    • [ICRA - Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [__`dep.`__ __`aut.`__]
    • [ICRA - Point Network for Classification of Deformed Building Element Point Clouds. [__`cls.`__]
    • [ICRA - Guided Geometry Extraction from Point Clouds. [__`oth.`__]
    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA - Based Exploration for Autonomous 3D Modeling. [__`oth.`__ __`aut.`__]
    • [ICRA - Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [__`oth.`__]
    • [ICRA - Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [__`tra.`__]
    • [ICRA
    • [ICRA - Cue Photometric Point Cloud Registration. [__`reg.`__]
    • [ICRA - Time SLAM for 3D Lidar-Based Online Mapping. [__`oth.`__]
    • [ICRA - LiDAR System. [__`oth.`__]
    • [ICRA
    • [ICRA - Sensor Fusion for 3D Mapping and Localization. [__`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Photogrammetry with Random Patterns. [__`rec.`__ __`oth.`__]
    • [IROS
    • [IROS
    • [IROS - Environment 3D LiDAR Localization. [__`oth.`__]
    • [IROS
    • [IROS - LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[[code](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM)] [__`pos.`__ __`oth.`__] :fire:
    • [IROS
    • [IROS
    • [IROS
    • [IROS - Cost RGB-D Sensor. [[code](https://github.com/CTTCGeoLab/VI_ROS)][__`oth.`__]
    • [IROS - Time Amodal 3D Object Detection. [__`det.`__ __`pos.`__]
    • [IROS
    • [IROS
    • [IROS
    • [TOG
    • [ITSC

  • 2019

    • [CVPR - Shape Convolutional Neural Network for Point Cloud Analysis. [[pytorch](https://github.com/Yochengliu/Relation-Shape-CNN)] [__`cls.`__ __`seg.`__ __`oth.`__] :fire:
    • [CVPR
    • [CVPR
    • [CVPR - LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. [[code](https://github.com/mileyan/pseudo_lidar)] [__`det.`__ __`dep.`__ __`aut.`__]
    • [CVPR
    • [CVPR - adv-pc)] [__`oth.`__]
    • [CVPR - Attention and Gumbel Subset Sampling. [__`cls.`__ __`seg.`__]
    • [CVPR - CNN: Annularly Convolutional Neural Networks on Point Clouds. [[tensorflow](https://github.com/artemkomarichev/a-cnn)][__`cls.`__ __`seg.`__]
    • [CVPR
    • [CVPR - Invariant Map Networks. [[tensorflow](https://github.com/zaiweizhang/path_invariance_map_network)] [__`seg.`__ __`oth.`__]
    • [CVPR - scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. [[code](https://github.com/daerduoCarey/partnet_dataset)] [__`dat.`__ __`seg.`__]
    • [CVPR
    • [CVPR
    • [CVPR
    • [CVPR - ziyan/SPH3D-GCN)] [__`cls.`__ __`seg.`__]
    • [CVPR
    • [CVPR - Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields. [[pytorch](https://github.com/pqhieu/JSIS3D)] [__`seg.`__]
    • [CVPR - Structured Deep Metric Learning. [__`seg.`__]
    • [CVPR
    • [CVPR - based Progressive 3D Point Set Upsampling. [[tensorflow](https://github.com/yifita/3PU)] [__`oth.`__]
    • [CVPR
    • [CVPR - grained and Hierarchical Shape Segmentation. [[pytorch](https://github.com/FoggYu/PartNet)] [__`dat.`__ __`seg.`__]
    • [CVPR
    • [CVPR - Based Randomized Approach for Robust Point Cloud Registration without Correspondences. [[matlab](https://github.com/intellhave/SDRSAC)] [__`reg.`__]
    • [CVPR - guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image. [__`rec.`__ __`oth.`__]
    • [CVPR
    • [CVPR - Capsule Networks. [[pytorch](https://github.com/yongheng1991/3D-point-capsule-networks)] [__`cls.`__ __`rec.`__ __`oth.`__]
    • [CVPR - Temporal ConvNets: Minkowski Convolutional Neural Networks. [[pytorch](https://github.com/StanfordVL/MinkowskiEngine)] [__`seg.`__] :fire:
    • [CVPR
    • [CVPR - Set Registration using Gaussian Filter and Twist Parameterization. [[code](https://bitbucket.org/gaowei19951004/poser/src/master/)] [__`reg.`__]
    • [CVPR
    • [CVPR - CNN. [__`cls.`__ __`det.`__]
    • [CVPR - Invariant Representation for Point Cloud Analysis. [__`cls.`__]
    • [CVPR - GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion. [[code](https://github.com/iSarmad/RL-GAN-Net)] [__`oth.`__]
    • [CVPR - Scale Outdoor Scenes. [[code](https://github.com/ziquan111/RobustPCLReconstruction)] [__`rec.`__]
    • [CVPR - Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks. [[tensorflow](https://github.com/sitzikbs/Nesti-Net)] [__`oth.`__]
    • [CVPR
    • [CVPR - to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer. [__`pos.`__]
    • [CVPR
    • [CVPR - 3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks. [[project](https://sites.google.com/view/lp-3dcnn/home)] [__`cls.`__ __`seg.`__]
    • [CVPR
    • [ICCV
    • [ICCV
    • [ICCV - GAN: a Point Cloud Upsampling Adversarial Network. [[tensorflow](https://github.com/liruihui/PU-GAN)] [__`oth.`__]
    • [ICCV
    • [ICCV - Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction. [__`oth.`__]
    • [ICCV - HandNet: Self-Organizing Network for 3D Hand Pose Estimation with Semi-supervised Learning. [[code](https://github.com/TerenceCYJ/SO-HandNet)] [__`pos.`__]
    • [ICCV - Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense. [__`oth.`__]
    • [ICCV - World Data. [__`cls.`__ __`dat.`__] [[code](https://github.com/hkust-vgd/scanobjectnn)] [[dataset](https://hkust-vgd.github.io/scanobjectnn/)]
    • [ICCV
    • [ICCV - vgd.github.io/shellnet/)] [__`seg.`__]
    • [ICCV - Based Multi-View Stereo Network. [[pytorch](https://github.com/callmeray/PointMVSNet)] [__`rec.`__]
    • [ICCV
    • [ICCV - to-End Deep Neural Network for 3D Point Cloud Registration. [__`reg.`__]
    • [ICCV
    • [ICCV - Edge Interaction Network for Point Cloud Semantic Segmentation. [__`seg.`__]
    • [ICCV
    • [ICCV - to-Dense 3D Object Detector for Point Cloud. [__`det.`__ __`oth.`__]
    • [ICCV - CNN. [__`det.`__ __`aut.`__]
    • [ICCVW
    • [NeurIPS - Supervised Deep Learning on Point Clouds by Reconstructing Space. [__`cls.`__ __`oth.`__]
    • [NeurIPS - Voxel CNN for Efficient 3D Deep Learning. [__`det.`__ __`seg.`__ __`aut.`__]
    • [ICLR
    • [ICMLW
    • [AAAI - iisc/capnet)] [__`rec.`__]
    • [AAAI - based Sequence to Sequence Network. [[tensorflow](https://github.com/liuxinhai/Point2Sequence)] [__`cls.`__ __`seg.`__]
    • [AAAI
    • [AAAI - View Relation Neural Network for 3D Shape Recognition. [[pytorch](https://github.com/Hxyou/PVRNet)] [__`cls.`__ __`rel.`__]
    • [TOG
    • [TOG
    • [SIGGRAPH Asia
    • [ICME
    • [ICASSP - Projection)] [__`oth.`__]
    • [BMVC - Shot Learning of 3D Objects. [__`cls.`__]
    • [ICRA - equ-net)] [__`cls.`__]
    • [ICRA - Object Segmentation from a LiDAR Point Cloud. [[tensorflow](https://github.com/xuanyuzhou98/SqueezeSegV2)] [__`seg.`__ __`aut.`__]
    • [ICRA
    • [ICRA
    • [ICRA - cloud-compression-by-RNN)] [__`oth.`__]
    • [ICRA - ram/FL3D)] [__`det.`__ __`aut.`__]
    • [ICRA
    • [ICRA - MatchNet: Learning to Match Keypoints across 2D Image and 3D Point Cloud. [__`oth.`__]
    • [ICRA - scale 5D Semantics Benchmark for Autonomous Driving. [[project](https://github.com/VCCIV/BLVD)] [__`dat.`__ __`det.`__ __`tra.`__ __`aut.`__ __`oth.`__]
    • [ICRA - overlap 3-D point cloud registration for outlier rejection. [[matlab](https://github.com/JStech/ICP)] [__`reg.`__]
    • [ICRA
    • [ICRA
    • [ICRA - Net: Multimodal VoxelNet for 3D Object Detection. [__`det.`__ __`aut.`__]
    • [ICRA - 3D: Estimating the Covariance of ICP in the Real World. [__`reg.`__]
    • [IROS - Aware PointNet for Object Recognition from Multi-View 2.5D Point Clouds. [[tensorflow](https://github.com/Merium88/Edge-Aware-PointNet)] [__`cls.`__ __`det.`__]
    • [IROS - Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles. [__`oth.`__] [__`aut.`__]
    • [IROS
    • [IV - to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving. [__`seg.`__] [__`aut.`__]
    • [Eurographics Workshop
    • [WACV
    • [3DV - vgd.github.io/riconv/)] [__`cls.`__ __`seg.`__]
    • [3DV - invariant Point CNN with Spherical Harmonics kernels. [[tensorflow](https://github.com/adrienPoulenard/SPHnet)] [__`cls.`__ __`seg.`__ __`oth.`__]
    • [TVCG - Selection of 3D Point Clouds. [[project](https://lassonet.github.io/)] [__`oth.`__]
    • [arXiv
    • [arXiv - Cloud Saliency Maps. [[tensorflow](https://github.com/tianzheng4/PointCloud-Saliency-Maps)] [__`cls.`__ __`oth.`__]
    • [arXiv - Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks)] [__`oth.`__]
    • [arxiv
    • [arXiv - Cloud to Image Translation using conditional Generative Adversarial Networks. [__`oth.`__]
    • [arXiv - Encoder and Sampler. [__`cls.`__ __`oth.`__]
    • [arXiv - aware Loss Function for Point Cloud Semantic Instance Segmentation. [__`seg.`__]
    • [arXiv - shot Learning of 3D Point Cloud Objects. [[code](https://github.com/alichr/Zero-shot-Learning-of-3D-Point-Cloud-Objects)] [__`cls.`__]
    • [arXiv - LiDAR Point Cloud. [__`det.`__ __`aut.`__]
    • [arXiv - time Multiple People Hand Localization in 4D Point Clouds. [__`det.`__ __`oth.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - View Proposal Generation for Real-Time Object Detection from Point Clouds. [[code](https://github.com/LordLiang/FVNet)] [__`det.`__ __`aut.`__]
    • [arXiv
    • [arXiv - Supervised Learning of Local Features in 3D Point Clouds. [__`cls.`__ __`seg.`__]
    • [arXiv
    • [arXiv - YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. [[pytorch](https://github.com/AI-liu/Complex-YOLO)] [__`det.`__ __`tra.`__ __`aut.`__] :fire:
    • [arXiv - based Inpainting for 3D Dynamic Point Clouds. [__`oth.`__]
    • [arXiv
    • [arXiv
    • [arXiv - AAE)] [__`rel.`__ __`oth.`__]
    • [arXiv
    • [arXiv
    • [arXiv - based Geometry Processing. [[pytorch](https://github.com/yifita/DSS)] [__`oth.`__]
    • [arXiv
    • [arXiv - Voxel CNN for Efficient 3D Deep Learning. [__`seg.`__ __`det.`__ __`aut.`__]
    • [arXiv - Equivariant Learning. [__`cls.`__]
    • [arXiv - Based Graphics. [[project](https://dmitryulyanov.github.io/neural_point_based_graphics)] [__`oth.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - 3D Siamese Networks on LIDAR. [__`tra.`__]
    • [arXiv
    • [arXiv - A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. [__`det.`__ __`aut.`__]
    • [arXiv
    • [arXiv
    • [arXiv - Decoder with Point Atrous Convolution for Unorganized 3D Points. [[tensorflow](https://github.com/paul007pl/PointAtrousGraph)] [__`cls.`__ __`seg.`__]
    • [arXiv
    • [arXiv - Rotation-Equivariant Quaternion Neural Networks. [__`cls.`__ __`rec.`__]
    • [arXiv - aware Capsules. [__`cls.`__ __`rel.`__ __`seg.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - GCN: Point Cloud Upsampling via Graph Convolutional Network. [[project](https://sites.google.com/kaust.edu.sa/pugcn)] [__`oth.`__]
    • [arXiv - GCN for Fast and Scalable Point Cloud Learning. [__`seg.`__ __`cls.`__]
    • [arXiv
    • [arXiv - Shot Learning for 3D Point Cloud Classification. [__`cls.`__]
    • [arXiv - Net)] [__`cls.`__ __`seg.`__]
    • [arvix - Point-Cloud)] [__`cls.`__ __`det.`__ __`tra.`__ __`seg.`__]
    • [arXiv - GANs for High-Resolution 3D Point-cloud Generation. [__`rec.`__ __`oth.`__]
    • [arXiv
    • [arXiv - LiDAR Point Cloud Interpolation. [__`oth.`__]
    • [arXiv - -- A Study of Point Cloud-Based Deep Learning Models. [__`cls.`__ __`det.`__]
    • [ICRA
    • [ICCV - Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis. [__`oth.`__ __`aut.`__]
    • [arXiv - BoNet)] [__`det.`__ __`seg.`__]
    • [CVPR
    • [CVPR - scale Point Clouds. [[pytorch](https://github.com/laoreja/HPLFlowNet)] [__`oth.`__]
    • [ICCV
    • [ICCV
    • [ICCV - Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction. [__`oth.`__]
    • [ICCV - Edge Interaction Network for Point Cloud Semantic Segmentation. [__`seg.`__]
    • [ICCV - to-Dense 3D Object Detector for Point Cloud. [__`det.`__ __`oth.`__]
    • [NeurIPS - Voxel CNN for Efficient 3D Deep Learning. [__`det.`__ __`seg.`__ __`aut.`__]
    • [IROS - Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles. [__`oth.`__] [__`aut.`__]
    • [3DV - vgd.github.io/riconv/)] [__`cls.`__ __`seg.`__]
    • [arXiv - Shot Learning for 3D Point Cloud Classification. [__`cls.`__]
    • [arXiv - Net)] [__`cls.`__ __`seg.`__]
    • [arXiv
    • [arXiv - Equivariant Learning. [__`cls.`__]
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv
    • [arXiv - Rotation-Equivariant Quaternion Neural Networks. [__`cls.`__ __`rec.`__]
    • [arXiv - aware Capsules. [__`cls.`__ __`rel.`__ __`seg.`__]
    • [arXiv - GCN: Point Cloud Upsampling via Graph Convolutional Network. [[project](https://sites.google.com/kaust.edu.sa/pugcn)] [__`oth.`__]
    • [arXiv - GCN for Fast and Scalable Point Cloud Learning. [__`seg.`__ __`cls.`__]
    • [NeurIPS - Supervised Deep Learning on Point Clouds by Reconstructing Space. [__`cls.`__ __`oth.`__]
    • [MM - Invariant Representations for Point Cloud Classification and Segmentation. [[tensorflow](https://github.com/tasx0823/SRINet)] [__`cls.`__ __`seg.`__]

  • 2017

    • [ICRA
    • [ICRA
    • [ICRA
    • [ICRA
    • [IROS
    • [IROS
    • [IROS
    • [IROS
    • [IROS
    • [3DV
    • [TPAMI - aware Data Consolidation. [__`oth.`__]
    • [ICCV - to-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors. [__`reg.`__]
    • [ICCV - Local Correspondence and Transformation Estimation. [__`reg.`__]
    • [AAAI - Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization. [__`reg.`__]
    • [TPAMI - aware Data Consolidation. [__`oth.`__]
    • [TPAMI - aware Data Consolidation. [__`oth.`__]
    • [TPAMI - aware Data Consolidation. [__`oth.`__]
    • [TPAMI - aware Data Consolidation. [__`oth.`__]
    • [TPAMI - aware Data Consolidation. [__`oth.`__]
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