# 3D-PointCloud

**Repository Path**: yrccry/PointCloud

## Basic Information

- **Project Name**: 3D-PointCloud
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2024-02-22
- **Last Updated**: 2024-02-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 3D - Point Cloud

**Paper list** and **Datasets** about Point Cloud. Datasets can be found in [Datasets.md](https://github.com/zhulf0804/3D-PointCloud/blob/master/Datasets.md).

<hr />

## Survey papers

- [3D Object Detection for Autonomous Driving: A Survey](https://arxiv.org/pdf/2106.10823.pdf) [arXiv 2021]
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://arxiv.org/pdf/2106.12735.pdf) [arXiv 2021]
- [3D Semantic Scene Completion: a Survey](https://arxiv.org/pdf/2103.07466.pdf) [arXiv 2021]
- [Deep Learning based 3D Segmentation: A Survey](https://arxiv.org/pdf/2103.05423.pdf) [arXiv 2021]
- [A comprehensive survey on point cloud registration](https://arxiv.org/pdf/2103.02690.pdf) [arXiv 2021]
- [Deep Learning for 3D Point Clouds: A Survey](https://arxiv.org/pdf/1912.12033.pdf) [TPAMI 2020]
- [A Comprehensive Performance Evaluation of 3D Local Feature Descriptors](https://www.researchgate.net/profile/Yulan_Guo/publication/274387466_A_Comprehensive_Performance_Evaluation_of_3D_Local_Feature_Descriptors/links/552b4a5b0cf29b22c9c1a6d2/A-Comprehensive-Performance-Evaluation-of-3D-Local-Feature-Descriptors.pdf) [IJCV 2016]

## 2021

- ICCV
  - [VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation](https://arxiv.org/pdf/2107.13824.pdf) [`seg`; [Github](https://github.com/hzykent/VMNet)]
  - [Learning with Noisy Labels for Robust Point Cloud Segmentation](https://arxiv.org/pdf/2107.14230.pdf) [`seg`; [Github](https://github.com/pleaseconnectwifi/PNAL)]
  - [Geometry Uncertainty Projection Network for Monocular 3D Object Detection](https://arxiv.org/pdf/2107.13774.pdf) [`det`, `monocular`]
  - [ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2107.11769.pdf) [`seg`]
  - [Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion](https://arxiv.org/pdf/2010.01089.pdf) [`pre-training`; [Github](https://github.com/hansen7/OcCo)]
  - [HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration](https://arxiv.org/pdf/2107.11992.pdf) [`registration`; [PyTorch](https://github.com/ispc-lab/HRegNet)]
  - [Score-Based Point Cloud Denoising](https://arxiv.org/pdf/2107.10981.pdf) [`denoising`]
  - [Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows](https://arxiv.org/pdf/2107.13788.pdf) [`monocular`, `pose`; [Github](https://github.com/twehrbein/Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows)]
- CVPR
  - [To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels](https://arxiv.org/pdf/2106.13381.pdf) [`det`]
  - [RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection](https://arxiv.org/pdf/2106.13365.pdf) [`det`]
  - [Point Cloud Upsampling via Disentangled Refinement](https://arxiv.org/pdf/2106.04779.pdf) [`upsampling`; [Github](https://github.com/liruihui/Dis-PU)]
  - [Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning](https://arxiv.org/pdf/2105.10203.pdf) [`seg`]
  - [Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts](https://arxiv.org/pdf/2012.09165.pdf) [`seg`; [PyTorch](https://github.com/facebookresearch/ContrastiveSceneContexts)]
  - [PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks](https://arxiv.org/pdf/1912.03264.pdf) [`upsampling`; [Tensorflow](https://github.com/guochengqian/PU-GCN)]
  - [Self-Point-Flow: Self-Supervised Scene Flow Estimation from Points Clouds with Optimal Transport and Random Walk](https://arxiv.org/pdf/2105.08248.pdf) [`scene flow`]
  - [SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data](https://arxiv.org/pdf/2105.08612.pdf) [`reconstruction`]
  - [HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding](https://arxiv.org/pdf/2105.07751.pdf) [`scene flow`]
  - [3D Spatial Recognition without Spatially Labeled 3D](https://arxiv.org/pdf/2105.06461.pdf) [`det`, `seg`]
  - [LASR: Learning Articulated Shape Reconstruction from a Monocular Video](https://arxiv.org/pdf/2105.02976.pdf) [`reconstruction`, `monocular`]
  - [VoxelContext-Net: An Octree based Framework for Point Cloud Compression](https://arxiv.org/pdf/2105.02158.pdf) [`compression`]
  - [Unsupervised 3D Shape Completion through GAN Inversion](https://arxiv.org/pdf/2104.13366.pdf) [`completion`; [PyTorch](https://junzhezhang.github.io/projects/ShapeInversion/)]
  - [KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control](https://arxiv.org/pdf/2104.11224.pdf) [[Github](https://github.com/tomasjakab/keypoint_deformer/)]
  - [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://arxiv.org/pdf/2104.09224.pdf) [`autonomous driving`; [PyTorch](https://github.com/autonomousvision/transfuser)]
  - [Self-Supervised Pillar Motion Learning for Autonomous Driving](https://arxiv.org/pdf/2104.08683.pdf) [`autonomous driving`; [Github](https://github.com/qcraftai/pillar-motion)]
  - [Variational Relational Point Completion Network](https://arxiv.org/pdf/2104.10154.pdf) [`completion`]
  - [Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds](https://arxiv.org/pdf/2104.06114.pdf) [`det`; [Github](https://github.com/cheng052/BRNet)]
  - [RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation](https://arxiv.org/pdf/2104.05328.pdf) [`registration`]
  - [Objects are Different: Flexible Monocular 3D Object Detection](https://arxiv.org/pdf/2104.02323.pdf) [`det`; [Github](https://github.com/zhangyp15/MonoFlex)]
  - [FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds](https://arxiv.org/pdf/2104.00798.pdf) [`scene flow`]
  - [HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection](https://arxiv.org/pdf/2104.00902.pdf) [`det`]
  - [Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation](https://arxiv.org/pdf/2103.14962.pdf) [`seg`; [PyTorch](https://github.com/edwardzhou130/Panoptic-PolarNet)]
  - [ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning](https://arxiv.org/pdf/2103.15231.pdf) [`reg`; [PyTorch](https://github.com/dornik/reagent)]
  - [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://arxiv.org/pdf/2103.15297.pdf) [`det`; [Github](https://github.com/TuSimple/LiDAR_RCNN)]
  - [Equivariant Point Network for 3D Point Cloud Analysis](https://arxiv.org/pdf/2103.14147.pdf) [[Github](https://github.com/nintendops/EPN_PointCloud)]
  - [PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds](https://arxiv.org/pdf/2103.14635.pdf) [`cls`, `det`; [Github](https://github.com/CVMI-Lab/PAConv)]
  - [Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection](https://arxiv.org/pdf/2103.16470.pdf) [`det`; [Github](https://github.com/fudan-zvg/DDMP)]
  - [Delving into Localization Errors for Monocular 3D Object Detection](https://arxiv.org/pdf/2103.16237.pdf) [`det`; [Github](https://github.com/xinzhuma/monodle)]
  - [M3DSSD: Monocular 3D Single Stage Object Detector](https://arxiv.org/pdf/2103.13164.pdf) [`det`; [Github](https://github.com/mumianyuxin/M3DSSD)]
  - [Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding](https://arxiv.org/pdf/2103.07838.pdf) [`completion`]
  - [Monte Carlo Scene Search for 3D Scene Understanding](https://arxiv.org/pdf/2103.07969.pdf)
  - [Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion](https://arxiv.org/pdf/2103.07074.pdf) [`seg`; [Github](https://github.com/ShiQiu0419/BAAF-Net)]
  - [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://arxiv.org/pdf/2103.05465.pdf) [`registration`; [PyTorch](https://github.com/XuyangBai/PointDSC)]
  - [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://arxiv.org/pdf/2103.05346.pdf) [`det`; [OpenPCDet](https://github.com/CVMI-Lab/ST3D)]
  - [Robust Point Cloud Registration Framework Based on Deep Graph Matching](https://arxiv.org/pdf/2103.04256.pdf) [`registration`; [Github](https://github.com/fukexue/RGM)]
  - [RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction](https://arxiv.org/pdf/2011.14744.pdf) [`reconstruction`]
  - [MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization](https://arxiv.org/pdf/2101.06605.pdf) [`motion analysis`; [Github](https://github.com/huangjh-pub/multibody-sync)]
  - [TPCN: Temporal Point Cloud Networks for Motion Forecasting](https://arxiv.org/pdf/2103.03067.pdf) [`motion forecasting`]
  - [Self-supervised Geometric Perception](https://arxiv.org/pdf/2103.03114.pdf) [`self-supervised`; [Github](https://github.com/theNded/SGP)]
  - [PointGuard: Provably Robust 3D Point Cloud Classification](https://arxiv.org/pdf/2103.03046.pdf) [`cls`]
  - [Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos](None)
  - [SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud](None) [`det`; [Github](https://github.com/Vegeta2020/SE-SSD)]
  - [Center-based 3D Object Detection and Tracking](https://arxiv.org/pdf/2006.11275.pdf) [`det`, `tracking`; [PyTorch](https://github.com/tianweiy/CenterPoint)]
  - [3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection](https://arxiv.org/pdf/2012.04355.pdf) [`det`; [PyTorch](https://github.com/thu17cyz/3DIoUMatch)]
  - [Style-based Point Generator with Adversarial Rendering for Point Cloud Completion](https://arxiv.org/pdf/2103.02535.pdf) [`completion`]
  - [FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation](https://arxiv.org/pdf/2103.02242.pdf) [`pose estimation`; [Github](https://github.com/ethnhe/FFB6D)]
  - [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://arxiv.org/pdf/2103.01458.pdf) [`generation`; [Github](https://github.com/luost26/diffusion-point-cloud)]
  - [GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation](https://arxiv.org/pdf/2102.12145.pdf) [`pose estimation`; [Github](https://github.com/THU-DA-6D-Pose-Group/GDR-Net)]
  - [PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers](https://arxiv.org/pdf/2011.13607.pdf) [`reconstruction`]
  - [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://arxiv.org/pdf/2011.13005.pdf) [`registration`; [PyTorch](https://github.com/ShengyuH/OverlapPredator)]
  - [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://arxiv.org/pdf/2011.12149.pdf) [`registration`; [Github](https://github.com/QingyongHu/SpinNet)]
  - [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://arxiv.org/pdf/2103.01100.pdf) [`det`]
  - [Multimodal Motion Prediction with Stacked Transformers](https://arxiv.org/pdf/2103.11624.pdf) [`motion prediction`; [Github](https://github.com/decisionforce/mmTransformer)]
  - [GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection](https://arxiv.org/pdf/2103.17202.pdf) [`det`; [PyTorch](https://github.com/abhi1kumar/groomed_nms)]
  - [Model-based 3D Hand Reconstruction via Self-Supervised Learning](https://arxiv.org/abs/2103.11703) [`reconstruction`]
  - [MonoRUn: Monocular 3D Object Detection by Self-Supervised Reconstruction and Uncertainty Propagation](https://arxiv.org/pdf/2103.12605.pdf) [`det`; [Github](https://github.com/tjiiv-cprg/MonoRUn)]
  - [Deep Implicit Moving Least-Squares Functions for 3D Reconstruction](https://arxiv.org/pdf/2103.12266.pdf) [`reconstruction`; [Tensorflow](https://github.com/Andy97/DeepMLS)]
  - [Skeleton Merger: an Unsupervised Aligned Keypoint Detector](https://arxiv.org/pdf/2103.10814.pdf) [`keypoint`; [PyTorch](https://github.com/eliphatfs/SkeletonMerger)]
  - [Single Image Depth Prediction with Wavelet Decomposition](https://arxiv.org/pdf/2106.02022.pdf) [`depth`; [PyTorch](https://github.com/nianticlabs/wavelet-monodepth)]
  - [3D Shape Generation with Grid-based Implicit Functions](https://arxiv.org/pdf/2107.10607.pdf) [`generation`]
- Others
  - [Similarity-Aware Fusion Network for 3D Semantic Segmentation](https://arxiv.org/pdf/2107.01579.pdf) [`seg`; [Github](https://github.com/lqzhao/SAFNet); IROS]
  - [Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning](https://arxiv.org/pdf/2107.01886.pdf) [`self-supervised`; ACM MM]
  - [Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting](https://arxiv.org/pdf/2107.02493.pdf) [`monocular`, `det`; ACM MM]
  - [Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline](https://arxiv.org/pdf/2106.05304.pdf) [`cls`; [PyTorch](https://github.com/princeton-vl/SimpleView); ICML]
  - [GIDSeg: Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors](https://arxiv.org/pdf/2105.12885.pdf) [`seg`; RAL]
  - [PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery](https://arxiv.org/pdf/2104.14769.pdf) [`sampling`; [Github](https://github.com/zwb0/PointLIE); IJCAI]
  - [Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective](https://arxiv.org/pdf/2104.09023.pdf) [`completion`; TOG]
  - [Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling](https://arxiv.org/pdf/2107.02389.pdf) [`seg`; TPAMI]
  - [Fast and Robust Iterative Closest Point](https://arxiv.org/pdf/2007.07627.pdf) [`registration`; [Github](https://github.com/yaoyx689/Fast-Robust-ICP); TPAMI]
  - [MonoGRNet: A General Framework for Monocular 3D Object Detection](https://arxiv.org/pdf/2104.08797.pdf) [`monocular`, `det`; TPAMI]
  - [Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network](https://arxiv.org/pdf/2103.16273.pdf) [`autonomous driving`; NeurIPS]
  - [PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds](https://arxiv.org/pdf/2103.02766.pdf) [`wireframe`; ICLR]
  - [Self-Guided Instance-Aware Network for Depth Completion and Enhancement](https://arxiv.org/pdf/2105.12186.pdf) [`depth`; ICRA]
  - [FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection](https://arxiv.org/pdf/2105.07647.pdf) [`det`; [Github](https://github.com/weiyithu/FGR); ICRA]
  - [Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks](https://arxiv.org/pdf/2103.15226.pdf) [`cls`, `seg`; ICRA]
  - [3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs](https://arxiv.org/pdf/2103.13808.pdf) [`keypoint`; [Github](https://github.com/ethz-asl/3d3l); ICRA]
  - [NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation](https://arxiv.org/pdf/2103.12292.pdf) [`localisation`; ICRA]
  - [Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation](https://arxiv.org/pdf/2103.12964.pdf) [`depth estimation`; ICRA]
  - [YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection](https://arxiv.org/pdf/2103.09422.pdf) [`det`; [PyTorch](https://github.com/Owen-Liuyuxuan/visualDet3D); ICRA]
  - [ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building](https://arxiv.org/pdf/2103.04316.pdf) [`static map`; ICRA]
  - [CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds](https://arxiv.org/pdf/2103.01977.pdf) [`pose estimation`; [Tensorflow](https://github.com/GeeeG/CloudAAE); ICRA]
  - [PointCutMix: Regularization Strategy for Point Cloud Classification](https://arxiv.org/pdf/2101.01461.pdf) [`cls`; [code](https://github.com/cuge1995/PointCutMix); ICML]
  - [Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud](https://arxiv.org/pdf/2103.10339.pdf) [`seg`; [Github](https://github.com/MingyeXu/IAF-Net); AAAI]
  - [PointINet: Point Cloud Frame Interpolation Network](https://arxiv.org/pdf/2012.10066.pdf) [`frame interpolation`; [PyTorch](https://github.com/ispc-lab/PointINet); AAAI]
  - [Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds](https://arxiv.org/pdf/2101.02381.pdf) [`seg`; [code](https://github.com/JchenXu/BoundaryAwareGEM); AAAI]
  - [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://arxiv.org/pdf/2012.15712.pdf) [`det`; AAAI]
  - [Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud](https://arxiv.org/pdf/2012.10921.pdf) [`cls`, `seg`; AAAI]
  - [CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud](https://arxiv.org/pdf/2012.03015.pdf) [`det`; [PyTorch](https://github.com/Vegeta2020/CIA-SSD); AAAI]
  - [Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion](https://arxiv.org/pdf/2012.03762.pdf) [`seg`; [Github](https://github.com/yanx27/JS3C-Net); AAAI]
  - [labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds](https://arxiv.org/pdf/2103.04970.pdf) [`labeling tool`; CAD]
- arXiv
  - [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://arxiv.org/pdf/2107.14160.pdf) [`det`; [Github](https://github.com/open-mmlab/mmdetection3d)]
  - [Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection](https://arxiv.org/pdf/2107.13931.pdf) [`det`; [Github](https://github.com/YinminZhang/MonoGeo)]
  - [CarveNet: Carving Point-Block for Complex 3D Shape Completion](https://arxiv.org/pdf/2107.13452.pdf) [`completion`]
  - [CKConv: Learning Feature Voxelization for Point Cloud Analysis](https://arxiv.org/pdf/2107.12655.pdf) [`cls`, `seg`]
  - [DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization](https://arxiv.org/pdf/2107.12707.pdf) [`det`]
  - [Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency](https://arxiv.org/pdf/2107.11355.pdf) [`det`]
  - [Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters](https://arxiv.org/pdf/2107.09783.pdf) [`seg`]
  - [Dynamic Convolution for 3D Point Cloud Instance Segmentation](https://arxiv.org/pdf/2107.08392.pdf)[`seg`; [PyTorch](https://github.com/aim-uofa/DyCo3D)]
  - [Beyond Farthest Point Sampling in Point-Wise Analysis](https://arxiv.org/pdf/2107.04291.pdf) [`sampling`]
  - [Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes](https://arxiv.org/pdf/2107.02972.pdf) [`seg`]
  - [Multi-Modality Task Cascade for 3D Object Detection](https://arxiv.org/pdf/2107.04013.pdf) [`det`; [Github](https://github.com/Divadi/MTC_RCNN)]
  - [Point Cloud Registration using Representative Overlapping Points](https://arxiv.org/pdf/2107.02583.pdf) [`registration`; [PyTorch](https://github.com/zhulf0804/ROPNet)]
  - [“Zero Shot” Point Cloud Upsampling](https://arxiv.org/pdf/2106.13765.pdf) [`upsampling`]
  - [Shape registration in the time of transformers](https://arxiv.org/pdf/2106.13679.pdf) [`registration`]
  - [3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching](https://arxiv.org/pdf/2106.11166.pdf) [`registration`]
  - [Image2Point: 3D Point-Cloud Understanding withPretrained 2D ConvNets](https://arxiv.org/pdf/2106.04180.pdf) [`cls`, `seg`; [Github](https://github.com/chenfengxu714/image2point)]
  - [FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration](https://arxiv.org/pdf/2106.03479.pdf) [`registration`]
  - [Z2P: Instant Rendering of Point Clouds](https://arxiv.org/pdf/2105.14548.pdf) [`rendering`]
  - [TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields](https://arxiv.org/pdf/2105.11605.pdf) [`place recognition`]
  - [Generalisable and distinctive 3D local
deep descriptors for point cloud registration](https://arxiv.org/pdf/2105.10382.pdf) [`registration`]
  - [Deep Weighted Consensus (DWC) Dense correspondence confidence maps for 3D shape registration](https://arxiv.org/pdf/2105.02714.pdf) [`registration`]
  - [M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers](https://arxiv.org/pdf/2104.11896.pdf) [`det`]
  - [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://arxiv.org/pdf/2104.10956.pdf) [`monocular`, `det`; [mmdet3D](https://github.com/open-mmlab/mmdetection3d)]
  - [Boundary-Aware 3D Object Detection from Point Clouds](https://arxiv.org/pdf/2104.10330.pdf) [`det`]
  - [Lidar Point Cloud Guided Monocular 3D Object Detection](https://arxiv.org/pdf/2104.09035.pdf) [`det`]
  - [Geometry-aware data augmentation for monocular 3D object detection](https://arxiv.org/pdf/2104.05858.pdf) [`det`]
  - [OCM3D: Object-Centric Monocular 3D Object Detection](https://arxiv.org/pdf/2104.06041.pdf) [`det`]
  - [Towards Efficient Graph Convolutional Networks for Point Cloud Handling](https://arxiv.org/pdf/2104.05706.pdf) [`network`; [Github](https://github.com/ofsoundof/EfficientGCN)]
  - [Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds](https://arxiv.org/pdf/2104.04724.pdf) [`scene flow`]
  - [A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds](https://arxiv.org/pdf/2104.05164.pdf) [`UDA`]
  - [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000× Fewer Labels](https://arxiv.org/pdf/2104.04891.pdf) [`seg`; [Github](https://github.com/QingyongHu/SQN)]
  - [View-Guided Point Cloud Completion](https://arxiv.org/pdf/2104.05666.pdf) [`completion`]
  - [One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation](https://arxiv.org/pdf/2104.02246.pdf) [`seg`]
  - [Potential Convolution: Embedding Point Clouds into Potential Fields](https://arxiv.org/pdf/2104.01754.pdf) [`cls`, `seg`]
  - [Group-Free 3D Object Detection via Transformers](https://arxiv.org/pdf/2104.00678.pdf) [`det`; [PyTorch](https://github.com/zeliu98/Group-Free-3D)]
  - [3D-MAN: 3D Multi-frame Attention Network for Object Detection](https://arxiv.org/pdf/2103.16054.pdf) [`det`]
  - [SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud](https://arxiv.org/pdf/2103.15396.pdf) [`det`; [Github](https://github.com/Liz66666/SIENet)]
  - [3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning](https://arxiv.org/pdf/2103.14533.pdf) [`registration`; [Github](https://github.com/humanpose1/MS-SVConv)]
  - [Multi-view 3D Reconstruction with Transformer](https://arxiv.org/pdf/2103.12957.pdf) [`reconstruction`]
  - [X-view: Non-egocentric Multi-View 3D Object Detector](https://arxiv.org/pdf/2103.13001.pdf) [`det`]
  - [RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation](https://arxiv.org/pdf/2103.12978.pdf) [`seg`]
  - [3DMNDT: 3D multi-view registration method based on the normal distributions transform](https://arxiv.org/pdf/2103.11084.pdf) [`registration`]
  - [RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection](https://arxiv.org/pdf/2103.10039.pdf) [`det`]
  - [SparsePoint: Fully End-to-End Sparse 3D Object Detector](https://arxiv.org/pdf/2103.10042.pdf) [`det`]
  - [S3Net: 3D LiDAR Sparse Semantic Segmentation Network](https://arxiv.org/pdf/2103.08745.pdf) [`seg`]
  - [Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions](https://arxiv.org/pdf/2103.08852.pdf) [`seg`]
  - [R-PointHop: A Green, Accurate and Unsupervised Point Cloud Registration Method](https://arxiv.org/pdf/2103.08129.pdf) [`registration`; [Github](https://github.com/pranavkdm/R-PointHop)]
  - [Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences](https://arxiv.org/pdf/2103.06028.pdf) [`autonomous driving`; [Github](https://github.com/TuSimple/LiDAR_SOT)]
  - [MapFusion: A General Framework for 3D Object Detection with HDMaps](https://arxiv.org/pdf/2103.05929.pdf) [`det`]
  - [Offboard 3D Object Detection from Point Cloud Sequences](https://arxiv.org/pdf/2103.05073.pdf) [`det`]
  - [A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding](https://arxiv.org/pdf/2103.04056.pdf) [`det`; [PyTorch](https://github.com/frankfengdi/LidarMTL)]
  - [IRON: Invariant-based Highly Robust Point Cloud Registration](https://arxiv.org/pdf/2103.04357.pdf) [`registration`]
  - [EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation](https://arxiv.org/pdf/2103.02517.pdf) [`cls`, `seg`]
  - [Pseudo-labeling for Scalable 3D Object Detection](https://arxiv.org/pdf/2103.02093.pdf) [`det`]
  - [LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment](https://arxiv.org/pdf/2103.02263.pdf) [`seg`]
  - [Scalable Scene Flow from Point Clouds in the Real World](https://arxiv.org/pdf/2103.01306.pdf) [`scene flow`]
  - [OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration](https://arxiv.org/pdf/2103.00937.pdf) [`registration`]
  - [InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring](https://arxiv.org/pdf/2103.01128.pdf) [`visual grounding`]
  - [FPS-Net: A Convolutional Fusion Network
  for Large-Scale LiDAR Point Cloud Segmentation](https://arxiv.org/pdf/2103.00738.pdf) [`seg`]
  - [P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching](https://arxiv.org/pdf/2103.01055.pdf) [`matching`]
  - [UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering](https://arxiv.org/pdf/2102.11870.pdf) [`registration`; [PyTorch](https://github.com/mbanani/unsupervisedRR)]
  - [Attention Models for Point Clouds in Deep Learning: A Survey](https://arxiv.org/pdf/2102.10788.pdf) [`attention`]
  - [EfficientLPS: Efficient LiDAR Panoptic
Segmentation](https://arxiv.org/pdf/2102.08009.pdf) [`seg`]
  - [HyperPocket: Generative Point Cloud Completion](https://arxiv.org/pdf/2102.05973.pdf) [`completion`]
  - [Point-set Distances for Learning Representations of 3D Point Clouds](https://arxiv.org/pdf/2102.04014.pdf) [`representation`]
  - [DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds](https://arxiv.org/pdf/2102.03747.pdf) [`det`]
  - [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://arxiv.org/pdf/2102.00463.pdf) [`det`; [OpenPCDet](https://github.com/open-mmlab/OpenPCDet)]
  - [Self-Attention Based Context-Aware 3D Object Detection](https://arxiv.org/pdf/2101.02672.pdf) [`det`; [PyTorch](https://github.com/AutoVision-cloud/SA-Det3D)]
  - [A two-stage data association approach for 3D Multi-object Tracking](https://arxiv.org/pdf/2101.08684.pdf) [`tracking`]
  - [The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions](https://arxiv.org/pdf/2101.07832.pdf) [`seg`]
  - [Joint Learning of 3D Shape Retrieval and Deformation](https://arxiv.org/pdf/2101.07889.pdf)
  - [Self-Supervised Pretraining of 3D Features on any Point-Cloud](https://arxiv.org/pdf/2101.02691.pdf) [`det`, `seg`, `cls`; [PyTorch](https://github.com/facebookresearch/DepthContrast)]
  - [Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition](https://arxiv.org/pdf/2101.02374.pdf) [`place recognition`; [Tensorflow](https://github.com/fpthink/EPC-Net)]

## 2020

- ECCV
  - [PointMixup: Augmentation for point cloud](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480341.pdf) [`augmentation`, `cls`; [PyTorch](https://github.com/yunlu-chen/PointMixup)]
  - [Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations](https://arxiv.org/pdf/2008.08766.pdf) [`det`; [PyTorch](https://github.com/AutoVision-cloud/Deformable-PV-RCNN)]
  - [Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700545.pdf) [`keypoints`]
  - [Weakly-supervised 3D Shape Completion in the Wild](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500273.pdf) [`completion`]
  - [SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480069.pdf) [`completion`, `cls`; [Github](https://github.com/wangyida/softpool)]
  - [Detail Preserved Point Cloud Completion via Separated Feature Aggregation](https://arxiv.org/pdf/2007.02374.pdf) [`completion`; [Tensorflow](https://github.com/XLechter/Detail-Preserved-Point-Cloud-Completion-via-SFA)]
  - [PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds](https://arxiv.org/pdf/1911.12408.pdf) [`flow estimation`; [PyTorch](https://github.com/DylanWusee/PointPWC)]
  - [JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds](https://arxiv.org/pdf/2007.06888.pdf) [`seg`; [Tensorflow](https://github.com/hzykent/JSENet)]
  - [A Closer Look at Local Aggregation Operators in Point Cloud Analysis](https://arxiv.org/pdf/2007.01294.pdf) [`cls`, `seg`; [Code](https://github.com/zeliu98/CloserLook3D)]
  - [Instance-Aware Embedding for Point Cloud Instance Segmentation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750256.pdf) [`seg`]
  - [Multimodal Shape Completion via Conditional Generative Adversarial Networks](https://arxiv.org/pdf/2003.07717.pdf) [`completion`; [PyTorch](https://github.com/ChrisWu1997/Multimodal-Shape-Completion)]
  - [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://arxiv.org/pdf/2006.03761.pdf) [`completion`; [PyTorch](https://github.com/hzxie/GRNet)]
  - [3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection](https://arxiv.org/pdf/2004.12636.pdf) [`det`]
  - [SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds](https://arxiv.org/pdf/2004.02774.pdf) [`det`; [Github](https://github.com/xinge008/SSN)]
  - [Pillar-based Object Detection for Autonomous Driving](https://arxiv.org/pdf/2007.10323.pdf) [`det`, `autonomous driving`; [Tensorflow](https://github.com/WangYueFt/pillar-od)]
  - [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection](https://arxiv.org/pdf/2007.08856.pdf) [`det`; [PyTorch](https://github.com/happinesslz/EPNet)]
  - [Finding Your (3D) Center: 3D Object Detection Using a Learned Loss](https://arxiv.org/pdf/2004.02693.pdf) [`det`; [Tensorflow](https://github.com/dgriffiths3/finding-your-center)]
  - [Weakly Supervised 3D Object Detection from Lidar Point Cloud](https://arxiv.org/pdf/2007.11901.pdf) [`det`; [PyTorch](https://github.com/hlesmqh/WS3D)]
  - [H3DNet: 3D Object Detection Using Hybrid Geometric Primitives](https://arxiv.org/pdf/2006.05682.pdf) [`det`; [Tensorflow](https://github.com/zaiweizhang/H3DNet)]
  - [Generative Sparse Detection Networks for 3D Single-shot Object Detection](https://arxiv.org/pdf/2006.12356.pdf) [`det`; [Github](https://github.com/jgwak/GSDN)]
  - [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://arxiv.org/pdf/2007.16100.pdf) [`seg`, `det`; [PyTorch](https://github.com/mit-han-lab/e3d)]
  - [DeepGMR: Learning Latent Gaussian Mixture Models for Registration](https://arxiv.org/pdf/2008.09088.pdf) [`registration`; [PyTorch](https://github.com/wentaoyuan/deepgmr)]
  - [Quaternion Equivariant Capsule Networks for 3D Point Clouds](https://arxiv.org/pdf/1912.12098.pdf) [[PyTorch](https://github.com/tolgabirdal/qecnetworks)]
  - [PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding](https://arxiv.org/pdf/2007.10985.pdf) [`unsupervised`; `cls`, `seg`, `det`; [PyTorch](https://github.com/facebookresearch/PointContrast)]
  - [Convolutional Occupancy Networks](https://arxiv.org/pdf/2003.04618.pdf) [`reconstruction`; [PyTorch](https://github.com/autonomousvision/convolutional_occupancy_networks)]
  - [Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration](https://arxiv.org/pdf/1910.10328.pdf) [`registration`; [PyTorch](https://github.com/jiahaowork/idam)]
  - [Progressive Point Cloud Deconvolution Generation Network](https://arxiv.org/pdf/2007.05361.pdf) [`generation`; [github](https://github.com/fpthink/PDGN)]
  - [Reinforced Axial Refinement Network for Monocular 3D Object Detection](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620528.pdf) [`det`, `monocular`]
  - [Monocular 3D Object Detection via Feature Domain Adaptation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540018.pdf) [`det`, `monocular`]
  - [Improving 3D Object Detection through Progressive Population Based Augmentation](https://arxiv.org/pdf/2004.00831.pdf) [`det`]
  - [An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds](https://arxiv.org/pdf/2007.12392.pdf) [`det`]
  - [Rotation-robust Intersection over Union for 3D Object Detection](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650460.pdf)
  - [DPDist: Comparing Point Clouds Using Deep Point Cloud Distance](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560528.pdf)
- CVPR
  - [PointPainting: Sequential Fusion for 3D Object Detection](https://openaccess.thecvf.com/content_CVPR_2020/papers/Vora_PointPainting_Sequential_Fusion_for_3D_Object_Detection_CVPR_2020_paper.pdf) [`det`]
  - [3DSSD: Point-based 3D Single Stage Object Detector](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_3DSSD_Point-Based_3D_Single_Stage_Object_Detector_CVPR_2020_paper.pdf) [`det`; [Tensorflow](https://github.com/Jia-Research-Lab/3DSSD)]
  - [A Hierarchical Graph Network for 3D Object Detection on Point Clouds](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.pdf) [`det`]
  - [Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence](https://openaccess.thecvf.com/content_CVPR_2020/papers/Donati_Deep_Geometric_Functional_Maps_Robust_Feature_Learning_for_Shape_Correspondence_CVPR_2020_paper.pdf) [`correspondences`; [Tensorflow](https://github.com/LIX-shape-analysis/GeomFmaps)]
  - [Deep Global Registration](https://arxiv.org/pdf/2004.11540.pdf) [`registration`; [PyTorch](https://github.com/chrischoy/DeepGlobalRegistration)]
  - [3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation](https://arxiv.org/pdf/2003.13867.pdf) [`seg`; [Github](https://github.com/francisengelmann/3D-MPA)]
  - [PointGMM: a Neural GMM Network for Point Clouds](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hertz_PointGMM_A_Neural_GMM_Network_for_Point_Clouds_CVPR_2020_paper.pdf) [`generation`, `registration`; [PyTorch](https://github.com/amirhertz/pointgmm)]
  - [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Point-GNN_Graph_Neural_Network_for_3D_Object_Detection_in_a_CVPR_2020_paper.pdf) [`det`; [Tensorflow](https://github.com/WeijingShi/Point-GNN)]
  - [ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qi_ImVoteNet_Boosting_3D_Object_Detection_in_Point_Clouds_With_Image_CVPR_2020_paper.pdf) [`det`]
  - [OccuSeg: Occupancy-aware 3D Instance Segmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Han_OccuSeg_Occupancy-Aware_3D_Instance_Segmentation_CVPR_2020_paper.pdf) [`seg`]
  - [Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation](https://arxiv.org/pdf/2003.06233.pdf) [`seg`; [PyTorch](https://github.com/jzhzhang/FusionAwareConv)]
  - [MLCVNet: Multi-Level Context VoteNet for 3D Object Detection](https://openaccess.thecvf.com/content_CVPR_2020/papers/Xie_MLCVNet_Multi-Level_Context_VoteNet_for_3D_Object_Detection_CVPR_2020_paper.pdf) [`det`; [PyTorch](https://github.com/NUAAXQ/MLCVNet)]
  - [Going Deeper with Lean Point Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Le_Going_Deeper_With_Lean_Point_Networks_CVPR_2020_paper.pdf) [`seg`; [PyTorch](https://github.com/erictuanle/GoingDeeperwPointNetworks)]
  - [Point Cloud Completion by Skip-attention Network with Hierarchical Folding](https://arxiv.org/pdf/2005.03871.pdf) [`completion`]
  - [Unsupervised Learning of Intrinsic Structural Representation Points](https://arxiv.org/pdf/2003.01661.pdf) [[PyTorch](https://github.com/NolenChen/3DStructurePoints)]
  - [PF-Net: Point Fractal Network for 3D Point Cloud Completion](https://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_PF-Net_Point_Fractal_Network_for_3D_Point_Cloud_Completion_CVPR_2020_paper.pdf) [`completion`; [PyTorch](https://github.com/zztianzz/PF-Net-Point-Fractal-Network)]
  - [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_PV-RCNN_Point-Voxel_Feature_Set_Abstraction_for_3D_Object_Detection_CVPR_2020_paper.pdf) [`det`; [code](https://github.com/open-mmlab/OpenPCDet)]
  - [Adaptive Hierarchical Down-Sampling for Point Cloud Classification](https://openaccess.thecvf.com/content_CVPR_2020/papers/Nezhadarya_Adaptive_Hierarchical_Down-Sampling_for_Point_Cloud_Classification_CVPR_2020_paper.pdf) [`downsampling`, `cls`]
  - [SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud](https://www4.comp.polyu.edu.hk/~cslzhang/paper/SA-SSD.pdf) [`det`; [PyTorch](https://github.com/skyhehe123/SA-SSD)]
  - [3DRegNet: A Deep Neural Network for 3D Point Registration](https://openaccess.thecvf.com/content_CVPR_2020/papers/Pais_3DRegNet_A_Deep_Neural_Network_for_3D_Point_Registration_CVPR_2020_paper.pdf) [`registration`; [Tensorflow](https://github.com/3DVisionISR/3DRegNet)]
  - [MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment](https://openaccess.thecvf.com/content_CVPR_2020/papers/Bernard_MINA_Convex_Mixed-Integer_Programming_for_Non-Rigid_Shape_Alignment_CVPR_2020_paper.pdf) [`non-rigid alignment`]
  - [SampleNet: Differentiable Point Cloud Sampling](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.pdf) [`sample`, `cls`, `registration`, `reconstruction`; [PyTorch](https://github.com/itailang/SampleNet)]
  - [Learning multiview 3D point cloud registration](https://arxiv.org/pdf/2001.05119.pdf) [`multiview registration`; [PyTorch](https://github.com/zgojcic/3D_multiview_reg)]
  - [Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences](https://arxiv.org/pdf/2005.01014.pdf) [`registration`; [PyTorch](https://github.com/XiaoshuiHuang/fmr)] 
  - [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://arxiv.org/pdf/2003.00492.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/yanx27/PointASNL)]
  - [Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds](https://arxiv.org/pdf/2003.12971.pdf) [`unsupervised`; `cls`; [PyTorch](https://github.com/raoyongming/PointGLR)]
  - [Grid-GCN for Fast and Scalable Point Cloud Learning](https://arxiv.org/pdf/1912.02984.pdf) [`cls`, `seg`; [mxnet](https://github.com/Xharlie/Grid-GCN)]
  - [FPConv: Learning Local Flattening for Point Convolution](https://arxiv.org/pdf/2002.10701.pdf) [`cls`, `seg`; [PyTorch](https://github.com/lyqun/FPConv)]
  - [PointAugment: an Auto-Augmentation Framework for Point Cloud Classification](https://arxiv.org/pdf/2002.10876.pdf) [`cls`, `retrieval`; [github](https://github.com/liruihui/PointAugment/)]
  - [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://arxiv.org/pdf/1911.11236.pdf) [`seg`; [Tensorflow](https://github.com/QingyongHu/RandLA-Net)]
  - [Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels](https://arxiv.org/pdf/2004.04091.pdf) [`weakly supervised`; `seg`; [Tensorflow](https://github.com/alex-xun-xu/WeakSupPointCloudSeg)]
  - [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://arxiv.org/pdf/2003.14032.pdf) [`seg`; [PyTorch](https://github.com/edwardzhou130/PolarSeg)]
  - [Learning to Segment 3D Point Clouds in 2D Image Space](https://arxiv.org/pdf/2003.05593.pdf) [`seg`; [Keras](https://github.com/Zhang-VISLab/Learning-to-Segment-3D-Point-Clouds-in-2D-Image-Space)]
  - [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://arxiv.org/pdf/2004.01658.pdf) [`seg`; [PyTorch](https://github.com/Jia-Research-Lab/PointGroup)]
  - [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://arxiv.org/pdf/2003.03164.pdf) [`keypoints`, `registration`; [Tensorflow](https://github.com/XuyangBai/D3Feat), [PyTorch](https://github.com/XuyangBai/D3Feat.pytorch)]
  - [RPM-Net: Robust Point Matching using Learned Features](https://arxiv.org/pdf/2003.13479.pdf) [`registration`; [PyTorch](https://github.com/yewzijian/RPMNet)]
  - [Cascaded Refinement Network for Point Cloud Completion](https://arxiv.org/pdf/2004.03327.pdf) [`completion`; [Tensorflow](https://github.com/xiaogangw/cascaded-point-completion)]
  - [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds](https://arxiv.org/pdf/2005.13888.pdf) [`tracking`; [PyTorch](https://github.com/HaozheQi/P2B)]
  - [An Efficient PointLSTM for Point Clouds Based Gesture Recognition](https://openaccess.thecvf.com/content_CVPR_2020/papers/Min_An_Efficient_PointLSTM_for_Point_Clouds_Based_Gesture_Recognition_CVPR_2020_paper.pdf) [`gesture`; [PyTorch](https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch)]
- Others
  - [Group Contextual Encoding for 3D Point Clouds](https://papers.nips.cc/paper/2020/file/9b72e31dac81715466cd580a448cf823-Paper.pdf) [`det`, `cls`; [PyTorch](https://github.com/AsahiLiu/PointDetectron); NeurIPS]
  - [CaSPR: Learning Canonical Spatiotemporal
  Point Cloud Representations](https://arxiv.org/pdf/2008.02792.pdf) [`dynamic sequences`; [Github](https://github.com/davrempe/caspr); NeurIPS]
  - [Skeleton-bridged Point Completion: From Global Inference to Local Adjustment](https://papers.nips.cc/paper/2020/file/ba036d228858d76fb89189853a5503bd-Paper.pdf) [`completion`; NeurIPS]
  - [Self-Supervised Few-Shot Learning on Point Clouds](https://arxiv.org/pdf/2009.14168.pdf) [`cls`, `seg`; NeurIPS]
  - [Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud](https://arxiv.org/pdf/2010.03318.pdf) [`cls`; NeurIPS]
  - [PIE-NET: Parametric Inference of Point Cloud Edges](https://arxiv.org/pdf/2007.04883.pdf) [`edge det`; NeurIPS]
  - [Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds](https://arxiv.org/pdf/1909.09287.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/hlei-ziyan/SPH3D-GCN); TPAMI]
  - [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://www.researchgate.net/profile/Hongsheng_Li4/publication/334316177_Part-A2_Net_3D_Part-Aware_and_Aggregation_Neural_Network_for_Object_Detection_from_Point_Cloud/links/5decd56f4585159aa46c201c/Part-A2-Net-3D-Part-Aware-and-Aggregation-Neural-Network-for-Object-Detection-from-Point-Cloud.pdf) [`det`; [PyTorch](https://github.com/open-mmlab/OpenPCDet); TPAMI]
  - [Unpaired Point Cloud Completion on Real Scans using Adversarial Training](https://arxiv.org/pdf/1904.00069.pdf) [`completion`; [Tensorflow](https://github.com/xuelin-chen/pcl2pcl-gan-pub); ICLR]
  - [AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing](https://arxiv.org/pdf/2002.00118.pdf) [`cls`, `seg`; [PyTorch](https://github.com/xingzhehe/AdvectiveNet-An-Eulerian-Lagrangian-Fluidic-Reservoir-for-Point-Cloud-Processing); ICLR]
  - [Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds](https://arxiv.org/pdf/1907.05279.pdf) [ICLR]
  - [MSN: Morphing and Sampling Network for Dense Point Cloud Completion](https://cseweb.ucsd.edu/~mil070/projects/AAAI2020/paper.pdf) [`completion`; [PyTorch](https://github.com/Colin97/MSN-Point-Cloud-Completion); AAAI]
  - [TANet: Robust 3D Object Detection from Point Clouds with Triple Attention](https://arxiv.org/pdf/1912.05163.pdf) [`det`; [PyTorch](https://github.com/happinesslz/TANet); AAAI]
  - [JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds](https://arxiv.org/pdf/1912.09654.pdf) [`seg`; [Tensorflow](https://github.com/dlinzhao/JSNet)]
  - [Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling](https://arxiv.org/pdf/1912.10775.pdf) [`cls`, `seg`; AAAI]
  - [Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution](https://arxiv.org/pdf/1811.09361.pdf) [`cls`, `seg`, `matching`; AAAI]
  - [Differentiable Manifold Reconstruction for Point Cloud Denoising](https://arxiv.org/pdf/2007.13551.pdf) [`denoising`; [PyTorch](https://github.com/luost26/DMRDenoise); ACM MM]
  - [Weakly Supervised 3D Object Detection from Point Clouds](https://arxiv.org/pdf/2007.13970.pdf) [`det`; [Tensorflow](https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection); ACM MM]
  - [Unsupervised Detection of Distinctive Regions on 3D Shapes](https://arxiv.org/pdf/1905.01684.pdf) [`unsupervised`; [Tensorflow](https://github.com/nini-lxz/Unsupervised-Shape-Distinction-Detection); TOG]
  - [Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds](https://arxiv.org/pdf/1907.12046.pdf) [`seg`, `cls`; [Project](https://francisengelmann.github.io/DPC/); ICRA]
  - [Semantic Graph Based Place Recognition for 3D Point Clouds](https://ras.papercept.net/proceedings/IROS20/0170.pdf) [`place recognition`; [PyTorch](https://github.com/kxhit/SG_PR); IROS]
  - [End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences](https://ras.papercept.net/proceedings/IROS20/1105.pdf) [`registration`; [PyTorch](https://github.com/qiaozhijian/VCR-Net); IROS]
  - [Correspondence Matrices are Underrated](https://arxiv.org/pdf/2010.16085.pdf) [`registration, correspondence`; [PyTorch](https://github.com/tzodge/PCR-CMU/tree/main/RPMNet_Code); 3DV]
  - [Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks](https://arxiv.org/abs/2101.00483) [`cls`, `seg`; 3DV]
  - [PanoNet3D: Combining Semantic and Geometric Understanding for LiDAR Point Cloud Detection](https://arxiv.org/pdf/2012.09418.pdf) [`det`; 3DV]
  - [FKAConv: Feature-Kernel Alignment for Point Cloud Convolution](https://openaccess.thecvf.com/content/ACCV2020/papers/Boulch_FKAConv_Feature-Kernel_Alignment_for_Point_Cloud_Convolution_ACCV_2020_paper.pdf) [`conv`, `cls`, `seg`; [PyTorch](https://github.com/valeoai/LightConvPoint); ACCV]
  - [Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks](https://openaccess.thecvf.com/content/ACCV2020/papers/Jack_Sparse_Convolutions_on_Continuous_Domains_for_Point_Cloud_and_Event_ACCV_2020_paper.pdf) [`conv`, `cls`; ACCV]
  - [Reconstructing Human Body Mesh from Point Clouds by Adversarial GP Network](https://openaccess.thecvf.com/content/ACCV2020/papers/Zhou_Reconstructing_Human_Body_Mesh_from_Point_Clouds_by_Adversarial_GP_ACCV_2020_paper.pdf) [`reconstruction`; ACCV]
  - [Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds](https://openaccess.thecvf.com/content/ACCV2020/papers/Wu_Bi-Directional_Attention_for_Joint_Instance_and_Semantic_Segmentation_in_Point_ACCV_2020_paper.pdf) [`seg`; [Tensorflow](https://github.com/pumpkinnan/BAN); ACCV]
  - [SDP-Net: Scene Flow Based Real-time Object Detection and Prediction from Sequential 3D Point Clouds](https://openaccess.thecvf.com/content/ACCV2020/papers/Zhang_SDP-Net_Scene_Flow_Based_Real-time_Object_Detection_and_Prediction_from_ACCV_2020_paper.pdf) [`det`; ACCV]
  - [Best Buddies Registration for Point Clouds](https://openaccess.thecvf.com/content/ACCV2020/papers/Drory_Best_Buddies_Registration_for_Point_Clouds_ACCV_2020_paper.pdf) [`registration`; [PyTorch](https://github.com/AmnonDrory/BestBuddiesRegistration); ACCV]
  - [HPGCNN: Hierarchical Parallel Group Convolutional Neural Networks for Point Clouds Processing](https://openaccess.thecvf.com/content/ACCV2020/papers/Dang_HPGCNN_Hierarchical_Parallel_Group_Convolutional_Neural_Networks_for_Point_Clouds_ACCV_2020_paper.pdf) [`conv`, `cls`, `seg`; ACCV]
  - [SAUM: Symmetry-Aware Upsampling Module for Consistent Point Cloud Completion](https://openaccess.thecvf.com/content/ACCV2020/papers/Son_SAUM_Symmetry-Aware_Upsampling_Module_for_Consistent_Point_Cloud_Completion_ACCV_2020_paper.pdf) [`completion`; [Tensorflow](https://github.com/countywest/SAUM); ACCV]
  - [Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features](https://www.researchgate.net/publication/340771096_Fast_and_Automatic_Registration_of_Terrestrial_Point_Clouds_Using_2D_Line_Features/fulltext/5ea05e5545851564fc34cb0c/Fast-and-Automatic-Registration-of-Terrestrial-Point-Clouds-Using-2D-Line-Features.pdf) [`registration`; Remote Sensing]
  - [ConvPoint: Continuous Convolutions for Point Cloud Processing](https://arxiv.org/pdf/1904.02375.pdf) [`cls`, `seg`; [PyTorch](https://github.com/aboulch/ConvPoint); Computers & Graphics]
- arXiv
  - [Multi-Modality Cut and Paste for 3D Object Detection](https://arxiv.org/pdf/2012.12741.pdf) [`det`; [PyTorch](https://github.com/open-mmlab/mmdetection3d)]
  - [Self-Supervised Learning for Domain Adaptation on Point Clouds](https://arxiv.org/pdf/2003.12641v3.pdf) [`cls`, `seg`]
  - [SALA: Soft Assignment Local Aggregation for 3D Semantic Segmentation](https://arxiv.org/pdf/2012.14929.pdf) [`seg`]
  - [CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds](https://arxiv.org/pdf/2012.15638.pdf) [`correspondence`]
  - [Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2012.14255.pdf) [`seg`]
  - [Geometric robust descriptor for 3D point cloud](https://arxiv.org/pdf/2012.12215.pdf) [`registration`, `cls`, `seg`]
  - [PCT: Point Cloud Transformer](https://arxiv.org/pdf/2012.09688.pdf) [`cls`, `seg`, `normal estimation`; [Jittor](https://github.com/MenghaoGuo/PCT)]
  - [Point Transformer(Hengshuang Zhao)](https://arxiv.org/pdf/2012.09164.pdf) [`seg`, `cls`; [PyTorch-unofficial](https://github.com/lucidrains/point-transformer-pytorch)]
  - [Point Transformer(Nico)](https://arxiv.org/pdf/2011.00931.pdf) [`cls`, `seg`]
  - [Deterministic PointNetLK for Generalized Registration](https://arxiv.org/pdf/2008.09527.pdf) [`registration`]
  - [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://arxiv.org/pdf/2011.10033.pdf) [`seg`; [PyTorch](https://github.com/xinge008/Cylinder3D)]
  - [OcCo: Pre-Training by Completing Point Clouds](https://arxiv.org/pdf/2010.01089.pdf) [`pre-training`, `completion`; [Github](https://github.com/hansen7/OcCo)]
  - [Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration](https://arxiv.org/pdf/2006.04523.pdf) [`registration`]
  - [PRE-TRAINING BY COMPLETING POINT CLOUDS](https://arxiv.org/pdf/2010.01089.pdf) [`pre-training`, `cls`, `seg`; [Github](https://github.com/hansen7/OcCo)]
  - [Continuous Geodesic Convolutions for Learning on 3D Shapes](https://arxiv.org/pdf/2002.02506.pdf) [`descriptor`, `match`, `seg`]
  - [Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation](https://arxiv.org/pdf/2009.08924.pdf) [`seg`]
  - [A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds](https://arxiv.org/pdf/2009.05307.pdf) [`det`]
  - [TEASER: Fast and Certifiable Point Cloud Registration](https://arxiv.org/pdf/2001.07715.pdf) [`registration`; [Github](https://github.com/MIT-SPARK/TEASER-plusplus)]
  - [Part-Aware Data Augmentation for 3D Object Detection in Point Cloud](https://arxiv.org/pdf/2007.13373.pdf) [`det`, `augmentation`; [PyTorch](https://github.com/sky77764/pa-aug.pytorch)]

## 2019

- ICCV
  - [Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning](https://openaccess.thecvf.com/content_ICCV_2019/papers/Hermosilla_Total_Denoising_Unsupervised_Learning_of_3D_Point_Cloud_Cleaning_ICCV_2019_paper.pdf) [`denoising`; [Tensorflow](https://github.com/phermosilla/TotalDenoising)]
  - [3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions](https://openaccess.thecvf.com/content_ICCV_2019/papers/Shu_3D_Point_Cloud_Generative_Adversarial_Network_Based_on_Tree_Structured_ICCV_2019_paper.pdf) [`generation`; [PyTorch](https://github.com/seowok/TreeGAN)]
  - [STD: Sparse-to-Dense 3D Object Detector for Point Cloud](https://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_STD_Sparse-to-Dense_3D_Object_Detector_for_Point_Cloud_ICCV_2019_paper.pdf) [`det`]
  - [USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds](https://openaccess.thecvf.com/content_ICCV_2019/papers/Li_USIP_Unsupervised_Stable_Interest_Point_Detection_From_3D_Point_Clouds_ICCV_2019_paper.pdf) [`keypoints`, `registration`; [PyTorch](https://github.com/lijx10/USIP)]
  - [LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and
  Environment Analysis](https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_LPD-Net_3D_Point_Cloud_Learning_for_Large-Scale_Place_Recognition_and_ICCV_2019_paper.pdf) [`place recognition`]
  - [Unsupervised Multi-Task Feature Learning on Point Clouds](https://openaccess.thecvf.com/content_ICCV_2019/papers/Hassani_Unsupervised_Multi-Task_Feature_Learning_on_Point_Clouds_ICCV_2019_paper.pdf) [`cls`, `seg`]
  - [Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction](https://arxiv.org/pdf/1907.12704.pdf) [`unsupervised`, `cls`, `generation`, `seg`, `completion`]
  - [SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences](https://openaccess.thecvf.com/content_ICCV_2019/papers/Behley_SemanticKITTI_A_Dataset_for_Semantic_Scene_Understanding_of_LiDAR_Sequences_ICCV_2019_paper.pdf) [`dataset`]
  - [MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences](https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_MeteorNet_Deep_Learning_on_Dynamic_3D_Point_Cloud_Sequences_ICCV_2019_paper.pdf) [`cls`, `seg`, `flow estimation`; [Tensorflow](https://github.com/xingyul/meteornet)]
  - [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://openaccess.thecvf.com/content_ICCV_2019/papers/Li_DeepGCNs_Can_GCNs_Go_As_Deep_As_CNNs_ICCV_2019_paper.pdf) [`seg`; [Tensorflow](https://github.com/lightaime/deep_gcns)]
  - [VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation](https://openaccess.thecvf.com/content_ICCV_2019/papers/Meng_VV-Net_Voxel_VAE_Net_With_Group_Convolutions_for_Point_Cloud_ICCV_2019_paper.pdf) [`seg`; [Github](https://github.com/xianyuMeng/VV-Net-Voxel-VAE-Net-with-Group-Convolutions-for-Point-Cloud-Segmentation)]
  - [Interpolated Convolutional Networks for 3D Point Cloud Understanding](https://openaccess.thecvf.com/content_ICCV_2019/papers/Mao_Interpolated_Convolutional_Networks_for_3D_Point_Cloud_Understanding_ICCV_2019_paper.pdf) [`cls`, `seg`]
  - [Dynamic Points Agglomeration for Hierarchical Point Sets Learning](https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Dynamic_Points_Agglomeration_for_Hierarchical_Point_Sets_Learning_ICCV_2019_paper.pdf) [`cls`, `seg`]
  - [ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics](https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_ShellNet_Efficient_Point_Cloud_Convolutional_Neural_Networks_Using_Concentric_Shells_ICCV_2019_paper.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/hkust-vgd/shellnet)]
  - [Fast Point R-CNN](https://arxiv.org/pdf/1908.02990.pdf) [`det`]
  - [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://openaccess.thecvf.com/content_ICCV_2019/papers/Uy_Revisiting_Point_Cloud_Classification_A_New_Benchmark_Dataset_and_Classification_ICCV_2019_paper.pdf) [`dataset`; `cls`; [Tensorflow](https://github.com/hkust-vgd/scanobjectnn)]
  - [KPConv: Flexible and Deformable Convolution for Point Clouds](https://openaccess.thecvf.com/content_ICCV_2019/papers/Thomas_KPConv_Flexible_and_Deformable_Convolution_for_Point_Clouds_ICCV_2019_paper.pdf) [`cls`, `seg`; [code](https://github.com/HuguesTHOMAS/KPConv)]
  - [Fully Convolutional Geometric Features](https://openaccess.thecvf.com/content_ICCV_2019/papers/Choy_Fully_Convolutional_Geometric_Features_ICCV_2019_paper.pdf) [`match`; [PyTorch](https://github.com/chrischoy/FCGF)]
  - [Deep Closest Point: Learning Representations for Point Cloud Registration](https://arxiv.org/pdf/1905.03304.pdf) [`registration`; [PyTorch](https://github.com/WangYueFt/dcp)]
  - [DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration](https://arxiv.org/pdf/1905.04153.pdf) [`registration`]
  - [Efficient and Robust Registration on the 3D Special Euclidean Group](https://openaccess.thecvf.com/content_ICCV_2019/papers/Bhattacharya_Efficient_and_Robust_Registration_on_the_3D_Special_Euclidean_Group_ICCV_2019_paper.pdf) [`registration`]
  - [Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation](https://arxiv.org/pdf/1909.10469.pdf) [`seg`]
  - [DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing](https://arxiv.org/pdf/1909.03669.pdf) [`cls`, `retrieval`, `seg`, `normal estimation`; [PyTorch](https://github.com/Yochengliu/DensePoint)]
  - [DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense](https://arxiv.org/pdf/1812.11017.pdf) [`cls`]
  - [Efficient Learning on Point Clouds with Basis Point Sets](https://arxiv.org/pdf/1908.09186.pdf) [`cls`, `registration`; [PyTorch](https://github.com/sergeyprokudin/bps)]
  - [PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows](https://arxiv.org/pdf/1906.12320.pdf) [`generation`, `reconstruction`; [Pytorch](https://github.com/stevenygd/PointFlow)
  - [PU-GAN: a Point Cloud Upsampling Adversarial Network](https://arxiv.org/pdf/1907.10844) [`upsampling`, `reconstruction`; [Project](https://liruihui.github.io/publication/PU-GAN/)]
  - [3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition](https://arxiv.org/pdf/1812.07050.pdf) [`retrieval`, `place recognition`]
  - [Deep Hough Voting for 3D Object Detection in Point Clouds](https://arxiv.org/pdf/1904.09664.pdf) [`det`; [PyTorch](https://github.com/facebookresearch/votenet)]
  - [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://arxiv.org/pdf/1904.08980.pdf) [`autonomous driving`; [Pytorch](https://github.com/felipecode/coiltraine)]
- CVPR
  - [LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yin_LiDAR-Based_Online_3D_Video_Object_Detection_With_Graph-Based_Message_Passing_CVPR_2020_paper.pdf) [`det`; [Github](https://github.com/yinjunbo/3DVID)]
  - [TopNet: Structural Point Cloud Decoder](https://openaccess.thecvf.com/content_CVPR_2019/papers/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.pdf) [`completion`; [Github](https://github.com/lynetcha/completion3d)]
  - [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_FlowNet3D_Learning_Scene_Flow_in_3D_Point_Clouds_CVPR_2019_paper.pdf) [`scene flow`; [Tensorflow](https://github.com/xingyul/flownet3d)]
  - [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://openaccess.thecvf.com/content_CVPR_2019/papers/Mescheder_Occupancy_Networks_Learning_3D_Reconstruction_in_Function_Space_CVPR_2019_paper.pdf) [`reconstruction`]
  - [Associatively Segmenting Instances and Semantics in Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Associatively_Segmenting_Instances_and_Semantics_in_Point_Clouds_CVPR_2019_paper.pdf) [`seg`; [Tensorflow](https://github.com/WXinlong/ASIS)]
  - [3D Point Capsule Networks](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_3D_Point_Capsule_Networks_CVPR_2019_paper.pdf) [`autoencoder`; [PyTorch](https://github.com/yongheng1991/3D-point-capsule-networks)]
  - [Patch-based Progressive 3D Point Set Upsampling](https://openaccess.thecvf.com/content_CVPR_2019/papers/Yifan_Patch-Based_Progressive_3D_Point_Set_Upsampling_CVPR_2019_paper.pdf) [`upsampling`; [Tensorflow](https://github.com/yifita/3PU), [PyTorch](https://github.com/yifita/3PU_pytorch)]
  - [Generating 3D Adversarial Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Xiang_Generating_3D_Adversarial_Point_Clouds_CVPR_2019_paper.pdf) [`adversary`; [Tensorflow](https://github.com/xiangchong1/3d-adv-pc)]
  - [RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion](https://openaccess.thecvf.com/content_CVPR_2019/papers/Sarmad_RL-GAN-Net_A_Reinforcement_Learning_Agent_Controlled_GAN_Network_for_Real-Time_CVPR_2019_paper.pdf) [`completion`; [PyTorch](https://github.com/iSarmad/RL-GAN-Net)]
  - [GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud](https://openaccess.thecvf.com/content_CVPR_2019/papers/Yi_GSPN_Generative_Shape_Proposal_Network_for_3D_Instance_Segmentation_in_CVPR_2019_paper.pdf) [`seg`; [Tensorflow](https://github.com/ericyi/GSPN)]
  - [JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields](https://openaccess.thecvf.com/content_CVPR_2019/papers/Pham_JSIS3D_Joint_Semantic-Instance_Segmentation_of_3D_Point_Clouds_With_Multi-Task_CVPR_2019_paper.pdf) [`seg`; [PyTorch](https://github.com/pqhieu/jsis3d)]
  - [3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans](https://openaccess.thecvf.com/content_CVPR_2019/papers/Hou_3D-SIS_3D_Semantic_Instance_Segmentation_of_RGB-D_Scans_CVPR_2019_paper.pdf) [`seg`; [PyTorch](https://github.com/Sekunde/3D-SIS)]
  - [Learning Transformation Synchronization](https://openaccess.thecvf.com/content_CVPR_2019/papers/Huang_Learning_Transformation_Synchronization_CVPR_2019_paper.pdf) [`transformation  synchronization`, `registration`; [PyTorch](https://github.com/xiangruhuang/Learning2Sync)]
  - [SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences](https://openaccess.thecvf.com/content_CVPR_2019/papers/Le_SDRSAC_Semidefinite-Based_Randomized_Approach_for_Robust_Point_Cloud_Registration_Without_CVPR_2019_paper.pdf) [`registration`; [Github](https://github.com/intellhave/SDRSAC)]
  - [Learning Transformation Synchronization](https://openaccess.thecvf.com/content_CVPR_2019/papers/Huang_Learning_Transformation_Synchronization_CVPR_2019_paper.pdf) [`reconstruction`; [PyTorch](https://github.com/xiangruhuang/Learning2Sync)]
  - [3D Local Features for Direct Pairwise Registration](https://openaccess.thecvf.com/content_CVPR_2019/papers/Deng_3D_Local_Features_for_Direct_Pairwise_Registration_CVPR_2019_paper.pdf) [`registration`]
  - [DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_DeepMapping_Unsupervised_Map_Estimation_From_Multiple_Point_Clouds_CVPR_2019_paper.pdf) [`registration`; [Github](https://github.com/ai4ce/DeepMapping)]
  - [Relation-Shape Convolutional Neural Network for Point Cloud Analysis](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Relation-Shape_Convolutional_Neural_Network_for_Point_Cloud_Analysis_CVPR_2019_paper.pdf) [`cls`, `seg`, `normal estimation`; [PyTorch](https://github.com/Yochengliu/Relation-Shape-CNN)]
  - [Modeling Local Geometric Structure of
  3D Point Clouds using Geo-CNN](https://openaccess.thecvf.com/content_CVPR_2019/papers/Lan_Modeling_Local_Geometric_Structure_of_3D_Point_Clouds_Using_Geo-CNN_CVPR_2019_paper.pdf) [`cls`, `det`; [Tensorflow](https://github.com/voidrank/Geo-CNN)]
  - [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://openaccess.thecvf.com/content_CVPR_2019/papers/Choy_4D_Spatio-Temporal_ConvNets_Minkowski_Convolutional_Neural_Networks_CVPR_2019_paper.pdf) [`seg`; [PyTorch](https://github.com/chrischoy/SpatioTemporalSegmentation)]
  - [PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_PCAN_3D_Attention_Map_Learning_Using_Contextual_Information_for_Point_CVPR_2019_paper.pdf) [`retrieval`; [Tensorflow](https://github.com/XLechter/PCAN)]
  - [Attentional PointNet for 3D-Object Detection in Point Clouds](https://openaccess.thecvf.com/content_CVPRW_2019/papers/WAD/Paigwar_Attentional_PointNet_for_3D-Object_Detection_in_Point_Clouds_CVPRW_2019_paper.pdf) [`det`; [PyTorch](https://github.com/anshulpaigwar/Attentional-PointNet)]
  - [Octree guided CNN with Spherical Kernels for 3D Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Lei_Octree_Guided_CNN_With_Spherical_Kernels_for_3D_Point_Clouds_CVPR_2019_paper.pdf) [`cls`, `seg`; [Github](https://github.com/hlei-ziyan/psicnn)]
  - [A-CNN: Annularly Convolutional Neural Networks on Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Komarichev_A-CNN_Annularly_Convolutional_Neural_Networks_on_Point_Clouds_CVPR_2019_paper.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/artemkomarichev/a-cnn)]
  - [ClusterNet: Deep Hierarchical Cluster Network with Rigorously Rotation-Invariant Representation for Point Cloud Analysis](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_ClusterNet_Deep_Hierarchical_Cluster_Network_With_Rigorously_Rotation-Invariant_Representation_for_CVPR_2019_paper.pdf) [`cls`]
  - [Graph Attention Convolution for Point Cloud Semantic Segmentation](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Graph_Attention_Convolution_for_Point_Cloud_Semantic_Segmentation_CVPR_2019_paper.pdf) [`seg`; [PyTorch-unofficial](https://github.com/yanx27/GACNet)]
  - [PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_PointWeb_Enhancing_Local_Neighborhood_Features_for_Point_Cloud_Processing_CVPR_2019_paper.pdf) [`seg`, `cls`; [PyTorch](https://github.com/hszhao/PointWeb)]
  - [Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling](https://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Modeling_Point_Clouds_With_Self-Attention_and_Gumbel_Subset_Sampling_CVPR_2019_paper.pdf) [`cls`, `seg`, `gesture`]
  - [Learning to Sample](https://arxiv.org/pdf/1812.01659.pdf) [`sample`, `cls`, `retrieval`, `reconstruction`; [Tensorflow](https://github.com/orendv/learning_to_sample)]
  - [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/DylanWusee/pointconv)]
  - [The Perfect Match: 3D Point Cloud Matching With Smoothed Densities](https://openaccess.thecvf.com/content_CVPR_2019/papers/Gojcic_The_Perfect_Match_3D_Point_Cloud_Matching_With_Smoothed_Densities_CVPR_2019_paper.pdf) [`match`; [code](https://github.com/zgojcic/3DSmoothNet)]
  - [PointNetLK: Point Cloud Registration using PointNet](https://arxiv.org/pdf/1903.05711.pdf) [`registration`; [PyTorch](https://github.com/hmgoforth/PointNetLK)]
  - [PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_PointRCNN_3D_Object_Proposal_Generation_and_Detection_From_Point_Cloud_CVPR_2019_paper.pdf) [`det`; [PyTorch](https://github.com/sshaoshuai/PointRCNN)]
  - [PointPillars: Fast Encoders for Object Detection From Point Clouds](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.pdf) [`det`; [Pytorch](https://github.com/nutonomy/second.pytorch)]
  - [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://arxiv.org/pdf/1812.07179.pdf) [`depth estimation`, `det`; [github](https://github.com/mileyan/pseudo_lidar)]
  - [ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving](https://arxiv.org/pdf/1811.12222.pdf) [`dataset`, `autonomous driving`]
  - [Stereo R-CNN based 3D Object Detection for Autonomous Driving](https://arxiv.org/pdf/1902.09738.pdf) [`det`, `autonomous driving`; [github](https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN)]
  - [Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://arxiv.org/pdf/1904.01690.pdf) [`det`, `autonomous driving`; [Tesorflow](https://github.com/kujason/monopsr)]
  - [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://arxiv.org/pdf/1903.08701.pdf) [`det`]
  - [GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving](https://arxiv.org/pdf/1903.10955.pdf) [`det`, `autonomous driving`]
  - [L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving](https://songshiyu01.github.io/pdf/L3Net_W.Lu_Y.Zhou_S.Song_CVPR2019.pdf) [`autonomous driving`]
  - [Iterative Transformer Network for 3D Point Cloud](https://arxiv.org/pdf/1811.11209.pdf) [`pose`, `cls`, `seg`; [Tensorflow](https://github.com/wentaoyuan/it-net)]
- Others
  - [PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation](https://arxiv.org/pdf/1911.02744.pdf) [`domain adaptation`; [PyTorch](https://github.com/canqin001/PointDAN); NeurIPS]
  - [Learning elementary structures for 3D shape generation and matching](https://papers.nips.cc/paper/2019/file/d360a502598a4b64b936683b44a5523a-Paper.pdf) [`generation`, `matching`; NeurIPS]
  - [Self-Supervised Deep Learning on Point Clouds by Reconstructing Space](https://papers.nips.cc/paper/2019/file/993edc98ca87f7e08494eec37fa836f7-Paper.pdf) [`self-supervised, cls, seg`; NeurIPS]
  - [Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://arxiv.org/pdf/1906.01140.pdf) [`seg`; [Tensorflow](https://github.com/Yang7879/3D-BoNet); NeurIPS]
  - [PRNet: Self-Supervised Learning for Partial-to-Partial Registration](http://papers.nips.cc/paper/9085-prnet-self-supervised-learning-for-partial-to-partial-registration.pdf) [`registration`, `cls`; [PyTorch](https://github.com/WangYueFt/prnet); NeurIPS]
  - [Point-Voxel CNN for Efficient 3D Deep Learning](https://arxiv.org/pdf/1907.03739.pdf) [`seg`, `det`; [PyTorch](https://github.com/mit-han-lab/pvcnn); NeurIPS]
  - [L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention](https://www.cs.umd.edu/~zwicker/publications/L2GAutoEncoder-ACMMM19.pdf) [`autoencoder`; ACM MM]
  - [Deep Cascade Generation on Point Sets](https://www.ijcai.org/Proceedings/2019/0517.pdf) [`generation`; [PyTorch](https://github.com/wkqscut/DCGNet); IJCAI]
  - [A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates](https://arxiv.org/pdf/1903.08588.pdf) [`registration`; RSS]
  - [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/pdf/1801.07829.pdf) [`cls`, `seg`; [Github](https://github.com/WangYueFt/dgcnn); TOG]
  - [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud](https://arxiv.org/pdf/1809.08495.pdf) [`seg`; [Tensorflow](https://github.com/xuanyuzhou98/SqueezeSegV2); ICRA]
  - [RangeNet++: Fast and Accurate LiDAR Semantic Segmentation](https://www.researchgate.net/profile/Cyrill_Stachniss/publication/338937998_RangeNet_Fast_and_Accurate_LiDAR_Semantic_Segmentation/links/5f609d4ba6fdcc1164133ac9/RangeNet-Fast-and-Accurate-LiDAR-Semantic-Segmentation.pdf) [`seg`; [PyTorch](https://github.com/PRBonn/lidar-bonnetal); IROS]
  - [AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects](https://arxiv.org/pdf/1910.04668.pdf) [`registration`; [Tensorflow](https://github.com/grossjohannes/AlignNet-3D); 3DV]
  - [Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network](https://val.cds.iisc.ac.in/papers/WACV19_Dense_PCR.pdf) [`reconstruction`; WACV]
- arXiv
  - [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://arxiv.org/pdf/1908.09492.pdf) [`det`]
  - [PCRNet: Point Cloud Registration Network using PointNet Encoding](https://arxiv.org/pdf/1908.07906.pdf) [`registration`; [PyTorch](https://github.com/vinits5/pcrnet_pytorch), [Tensorflow](https://github.com/vinits5/pcrnet)]
  - [LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer](https://arxiv.org/pdf/1905.05442.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/LinZhuoChen/LSANet)]
  - [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://arxiv.org/pdf/1907.06826.pdf) [`autonomous driving`]
  - [Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features](https://arxiv.org/pdf/1904.10014.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/KuangenZhang/ldgcnn)]

## 2018

- CVPR
  - [Learning 3D Shape Completion From Laser Scan Data With Weak Supervision](https://openaccess.thecvf.com/content_cvpr_2018/papers/Stutz_Learning_3D_Shape_CVPR_2018_paper.pdf) [`completion`; [Github](https://github.com/davidstutz/cvpr2018-shape-completion)]
  - [Deep Parametric Continuous Convolutional Neural Networks](https://openaccess.thecvf.com/content_cvpr_2018/papers_backup/Wang_Deep_Parametric_Continuous_CVPR_2018_paper.pdf) [`seg`, `motion estimation(lidar flow)`]
  - [Attentional ShapeContextNet for Point Cloud Recognition](http://pages.ucsd.edu/~ztu/publication/cvpr18_ascn.pdf) [`cls`, `seg`]
  - [A Papier-Mâché Approach to Learning 3D Surface Generation](https://openaccess.thecvf.com/content_cvpr_2018/papers/Groueix_A_Papier-Mache_Approach_CVPR_2018_paper.pdf) [`generation`; [PyTorch](https://github.com/ThibaultGROUEIX/AtlasNet)]
  - [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://openaccess.thecvf.com/content_cvpr_2018/papers/Landrieu_Large-Scale_Point_Cloud_CVPR_2018_paper.pdf) [`seg`; [PyTorch](https://github.com/loicland/superpoint_graph)]
  - [FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation](https://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.pdf) [`autoencoder`, `unsupervised`; [code](https://www.merl.com/research/license#FoldingNet)]
  - [FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis](https://arxiv.org/pdf/1706.05206.pdf) [`correspondence`, `seg`; [Tensorflow](https://github.com/nitika-verma/FeaStNet)]
  - [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://openaccess.thecvf.com/content_cvpr_2018/papers/Uy_PointNetVLAD_Deep_Point_CVPR_2018_paper.pdf) [`retrieval`, `place recognition`; [Tensorflow](https://github.com/mikacuy/pointnetvlad)]
  - [PU-Net: Point Cloud Upsampling Network](https://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_PU-Net_Point_Cloud_CVPR_2018_paper.pdf) [`upsampling`; [Tensorflow](https://github.com/yulequan/PU-Net)]
  - [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_SGPN_Similarity_Group_CVPR_2018_paper.pdf) [`seg`; [Tensorflow](https://github.com/laughtervv/SGPN)]
  - [Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling](https://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1573.pdf) [`cls`, `seg`; [code](http://www.merl.com/research/license#KCNet)]
  - [Tangent Convolutions for Dense Prediction in 3D](http://vladlen.info/papers/tangent-convolutions.pdf) [`seg`; [Tensorflow](https://github.com/tatarchm/tangent_conv)]
  - [PointGrid: A Deep Network for 3D Shape Understanding](https://openaccess.thecvf.com/content_cvpr_2018/papers/Le_PointGrid_A_Deep_CVPR_2018_paper.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/trucleduc/PointGrid)]
  - [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://openaccess.thecvf.com/content_cvpr_2018/papers/Graham_3D_Semantic_Segmentation_CVPR_2018_paper.pdf) [`seg`; [Github](https://github.com/facebookresearch/SparseConvNet)]
  - [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://openaccess.thecvf.com/content_cvpr_2018/papers/Landrieu_Large-Scale_Point_Cloud_CVPR_2018_paper.pdf) [`seg`; [PyTorch](https://github.com/loicland/superpoint_graph)]
  - [SPLATNet: Sparse Lattice Networks for Point Cloud Processing](https://openaccess.thecvf.com/content_cvpr_2018/papers/Su_SPLATNet_Sparse_Lattice_CVPR_2018_paper.pdf) [`seg`; [Caffe](https://github.com/NVlabs/splatnet)]
  - [Pointwise Convolutional Neural Networks](https://openaccess.thecvf.com/content_cvpr_2018/papers/Hua_Pointwise_Convolutional_Neural_CVPR_2018_paper.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/hkust-vgd/pointwise)]
  - [SO-Net: Self-Organizing Network for Point Cloud Analysis](https://openaccess.thecvf.com/content_cvpr_2018/papers/Li_SO-Net_Self-Organizing_Network_CVPR_2018_paper.pdf) [`autoencoder`, `cls`, `seg`; [PyTorch](https://github.com/lijx10/SO-Net)]
  - [Recurrent Slice Networks for 3D Segmentation of Point Clouds](https://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Recurrent_Slice_Networks_CVPR_2018_paper.pdf) [`seg`; [PyTorch](https://github.com/qianguih/RSNet/blob/master/README.md)]
  - [PPFNet: Global Context Aware Local Features for Robust 3D Point Matching](https://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_PPFNet_Global_Context_CVPR_2018_paper.pdf) [`registration`]
  - [PIXOR: Real-Time 3D Object Detection From Point Clouds](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3D_CVPR_2018_paper.pdf) [`det`; [PyTorch](https://github.com/philip-huang/PIXOR)]
  - [Frustum PointNets for 3D Object Detection From RGB-D Data](http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf) [`det`; [Tensorflow](https://github.com/charlesq34/frustum-pointnets)]		
  - [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_VoxelNet_End-to-End_Learning_CVPR_2018_paper.pdf) [`det`]
  - [3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare](http://openaccess.thecvf.com/content_cvpr_2018/papers/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.pdf) [`reconstruction`]
  - [Multi-Level Fusion Based 3D Object Detection From Monocular Images](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Multi-Level_Fusion_Based_CVPR_2018_paper.pdf) [`det`]
- ECCV
  - [3D-CODED : 3D Correspondences by Deep Deformation](http://www.vovakim.com/papers/18_ECCV_3DCODED.pdf) [`matching`; [PyTorch](https://github.com/ThibaultGROUEIX/3D-CODED)]
  - [SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters](https://openaccess.thecvf.com/content_ECCV_2018/papers/Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/xyf513/SpiderCNN)]
  - [3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Zeng_3DContextNet_K-d_Tree_Guided_Hierarchical_Learning_of_Point_Clouds_Using_ECCVW_2018_paper.pdf) [`seg`, `cls`]
  - [Multiresolution Tree Networks for
  3D Point Cloud Processing](https://openaccess.thecvf.com/content_ECCV_2018/papers/Matheus_Gadelha_Multiresolution_Tree_Networks_ECCV_2018_paper.pdf) [`cls`, `generation`; [PyTorch](https://github.com/matheusgadelha/MRTNet)]
  - [HGMR: Hierarchical Gaussian Mixtures for
  Adaptive 3D Registration](https://openaccess.thecvf.com/content_ECCV_2018/papers/Benjamin_Eckart_Fast_and_Accurate_ECCV_2018_paper.pdf) [`registration`; [unofficial code](https://github.com/somanshu25/GPU-Accelerated-Point-Cloud-Registration-Using-Hierarchical-GMM)]
  - [EC-Net: an Edge-aware Point set Consolidation Network](https://arxiv.org/pdf/1807.06010.pdf) [`consolidation`; [Tensorflow](https://github.com/yulequan/EC-Net)]
  - [Learning and Matching Multi-View Descriptors for Registration of Point Clouds](https://openaccess.thecvf.com/content_ECCV_2018/papers/Lei_Zhou_Learning_and_Matching_ECCV_2018_paper.pdf) [`registration`]
  - [Local Spectral Graph Convolution for Point Set Feature Learning](https://arxiv.org/pdf/1803.05827.pdf) [`cls`, `seg`]
  - [3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaoqing_Ye_3D_Recurrent_Neural_ECCV_2018_paper.pdf) [`seg`]
  - [Fully-Convolutional Point Networks for Large-Scale Point Clouds](https://openaccess.thecvf.com/content_ECCV_2018/papers/Dario_Rethage_Fully-Convolutional_Point_Networks_ECCV_2018_paper.pdf) [`seg`, `captioning`; [Tensorflow](https://github.com/drethage/fully-convolutional-point-network)]
  - [PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors](https://openaccess.thecvf.com/content_ECCV_2018/papers/Tolga_Birdal_PPF-FoldNet_Unsupervised_Learning_ECCV_2018_paper.pdf) [`registration`; [PyTorch-unofficial](https://github.com/XuyangBai/PPF-FoldNet)]
  - [Deep Continuous Fusion for Multi-Sensor 3D Object Detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf) [`det`]
  - [3DFeat-Net: Weakly Supervised Local 3D
  Features for Point Cloud Registration](https://openaccess.thecvf.com/content_ECCV_2018/papers/Zi_Jian_Yew_3DFeat-Net_Weakly_Supervised_ECCV_2018_paper.pdf) [`match`, `registration`; [Tensorflow](https://github.com/yewzijian/3DFeatNet)]
  - [Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving](http://openaccess.thecvf.com/content_ECCV_2018/papers/Peiliang_LI_Stereo_Vision-based_Semantic_ECCV_2018_paper.pdf) [`autonomous driving`]
- Others
  - [PointCNN: Convolution On X -Transformed Points](http://papers.neurips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/yangyanli/PointCNN); NeurIPS]
  - [Learning Representations and Generative Models for 3D Point Clouds](https://arxiv.org/pdf/1707.02392.pdf) [`autoencoder`; [Tensorflow](https://github.com/optas/latent_3d_points); ICML]
  - [RGCNN: Regularized Graph CNN for Point Cloud Segmentation](https://arxiv.org/pdf/1806.02952.pdf) [`seg`, `cls`; [Tensorflow](https://github.com/tegusi/RGCNN); ACM MM]
  - [PCN: Point Completion Network](https://arxiv.org/pdf/1808.00671.pdf) [`completion`; [Tensorflow](https://github.com/wentaoyuan/pcn); 3DV]
  - [Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration](https://arxiv.org/pdf/1808.03899.pdf) [`registration`; 3DV]
  - [Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods](https://arxiv.org/pdf/1808.00495.pdf) [`seg`; 3DV]
  - [Guaranteed Outlier Removal for Point Cloud Registration with Correspondences](https://www.researchgate.net/profile/Alvaro_Parra4/publication/321074557_Guaranteed_Outlier_Removal_for_Point_Cloud_Registration_with_Correspondences/links/5d2700bd92851cf440790ce5/Guaranteed-Outlier-Removal-for-Point-Cloud-Registration-with-Correspondences.pdf) [`registration`; TPAMI]
  - [Second: Sparsely embedded convolutional detection](https://www.mdpi.com/1424-8220/18/10/3337/pdf) [`det`; `Sensors`]	
  - [Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving](https://ieeexplore.ieee.org/abstract/document/#) [`det`, `autonomous driving`; IEEE Robotics and Automation Letters]
  - [HDNET: Exploiting HD Maps for 3D Object Detection](http://proceedings.mlr.press/v87/yang18b/yang18b.pdf) [`det`, `autonomous driving`; CoRL]
  - [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://arxiv.org/pdf/1712.02294.pdf) [`det`, `autonomous driving`; IROS]
  - [Flex-Convolution(Million-Scale Point-Cloud Learning Beyond Grid-Worlds)](https://arxiv.org/pdf/1803.07289.pdf) [`cls`, `seg`; [Tensorflow](https://github.com/cgtuebingen/Flex-Convolution); ACCV]
  - [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://arxiv.org/pdf/1710.07368.pdf) [`seg`; [Tensorflow](https://github.com/BichenWuUCB/SqueezeSeg); ICRA]
  - [Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds](https://arxiv.org/pdf/1806.01759.pdf) [`seg`, `cls`, `normal estimation`; [Tensorflow](https://github.com/viscom-ulm/MCCNN); TOG]
  - [Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction](https://arxiv.org/pdf/1706.07036.pdf) [`reconstruction`; [Tensorflow](https://github.com/chenhsuanlin/3D-point-cloud-generation); AAAI]
- arXiv
  - [Spherical Convolutional Neural Network
  for 3D Point Clouds](https://arxiv.org/pdf/1805.07872.pdf) [`cls`]
  - [Point Convolutional Neural Networks by Extension Operators](https://arxiv.org/pdf/1803.10091.pdf) [`cls`, `seg`, `normal estimation`; [Tensorflow](https://github.com/matanatz/pcnn)]
  - [PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/1807.00652.pdf) [`seg`; [Tensorflow](https://github.com/MVIG-SJTU/pointSIFT)]
  - [Point Cloud GAN](https://arxiv.org/pdf/1810.05795.pdf) [`generation`; [PyTorch](https://github.com/chunliangli/Point-Cloud-GAN)]
  - [Roarnet: A robust 3d object detection based on region approximation refinement](https://arxiv.org/pdf/1811.03818.pdf) [`det`]
  - [Complex-YOLO: Real-time 3D Object Detection on Point Clouds](https://arxiv.org/pdf/1803.06199) [`det`; [PyTorch](https://github.com/AI-liu/Complex-YOLO)]
  - [Classification of Point Cloud Scenes with Multiscale Voxel Deep Network](https://arxiv.org/pdf/1804.03583.pdf) [`seg`]

## 2017

- CVPR
  - [Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis](https://arxiv.org/pdf/1612.00101.pdf) [`completion`; [Torch7](https://github.com/angeladai/cnncomplete)]
  - [SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation](https://openaccess.thecvf.com/content_cvpr_2017/papers/Yi_SyncSpecCNN_Synchronized_Spectral_CVPR_2017_paper.pdf) [`seg`, `keypoints`; [Github](https://github.com/ericyi/SyncSpecCNN)]
  - [A Point Set Generation Network for 3D Object Reconstruction From a Single Image](https://openaccess.thecvf.com/content_cvpr_2017/papers/Fan_A_Point_Set_CVPR_2017_paper.pdf) [`reconstruction`; [Tensorflow](https://github.com/fanhqme/PointSetGeneration)]
  - [Multi-View 3D Object Detection Network for Autonomous Driving](http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Multi-View_3D_Object_CVPR_2017_paper.pdf) [`det`, `autonomous driving`; [Tensorflow](https://github.com/bostondiditeam/MV3D)]
  - [Deep MANTA: A Coarse-To-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis From Monocular Image](http://openaccess.thecvf.com/content_cvpr_2017/papers/Chabot_Deep_MANTA_A_CVPR_2017_paper.pdf) [`autonomous driving`]
  - **[PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf)** [`cls`, `seg`; [Tensorflow](https://github.com/charlesq34/pointnet)]
  - [3D Bounding Box Estimation Using Deep Learning and Geometry](http://openaccess.thecvf.com/content_cvpr_2017/papers/Mousavian_3D_Bounding_Box_CVPR_2017_paper.pdf) [`det`]
  - [OctNet: Learning Deep 3D Representations at High Resolutions](http://openaccess.thecvf.com/content_cvpr_2017/papers/Riegler_OctNet_Learning_Deep_CVPR_2017_paper.pdf) [`cls`, `seg`, `orientation estimation`; [PyTorch](https://github.com/griegler/octnet)]
  - [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://openaccess.thecvf.com/content_cvpr_2017/papers/Zeng_3DMatch_Learning_Local_CVPR_2017_paper.pdf) [`match`, `registration`; [project](http://3dmatch.cs.princeton.edu/)]
  - [3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder](https://openaccess.thecvf.com/content_cvpr_2017/papers/Elbaz_3D_Point_Cloud_CVPR_2017_paper.pdf) [`registration`; [github](https://github.com/gilbaz/LORAX)]
- ICCV
  - [High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference](https://openaccess.thecvf.com/content_ICCV_2017/papers/Han_High-Resolution_Shape_Completion_ICCV_2017_paper.pdf) [`completion`]
  - [Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models](https://arxiv.org/pdf/1704.01222.pdf) [`cls`, `retrieval`, `seg`; [PyTorch-unofficial](https://github.com/fxia22/kdnet.pytorch)]
  - [Learning Compact Geometric Features](https://openaccess.thecvf.com/content_ICCV_2017/papers/Khoury_Learning_Compact_Geometric_ICCV_2017_paper.pdf) [`registration`; [Github](https://github.com/marckhoury/CGF)]
  - [2D-Driven 3D Object Detection in RGB-D Images](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.pdf) [`det`]
- Others
  - **[Pointnet++: Deep hierarchical feature learning on point sets in a metric space](http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf)** [`cls`, `seg`; [Tensorflow](https://github.com/charlesq34/pointnet2); NIPS]
  - [Deep Sets](https://papers.nips.cc/paper/2017/file/f22e4747da1aa27e363d86d40ff442fe-Paper.pdf) [[PyTorch](https://github.com/manzilzaheer/DeepSets); `cls`]
  - [3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection](https://arxiv.org/pdf/1608.07711.pdf) [`det`, `autonomous driving`; TPAMI]
  - [O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis](https://arxiv.org/pdf/1712.01537.pdf) [`cls`, `retrieval`, `seg`; [Github](https://github.com/microsoft/O-CNN); TOG]
  - [Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks](https://arxiv.org/pdf/1609.06666.pdf) [`det`; ICRA]
  - [3d fully convolutional network for vehicle detection in point cloud](https://arxiv.org/pdf/1611.08069.pdf) [`det`; [Tensorflow](https://github.com/yukitsuji/3D_CNN_tensorflow); IROS]
  - [Shape Completion Enabled Robotic Grasping](https://arxiv.org/pdf/1609.08546.pdf) [`completion`; [Keras](https://github.com/CRLab/pc_object_completion_cnn); IROS]
  - [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://arxiv.org/pdf/1710.07563.pdf) [`seg`; 3DV]

## Before 2016

- 2016
  - [Fast Global Registration](https://www.researchgate.net/profile/Vladlen_Koltun/publication/305983982_Fast_Global_Registration/links/57a8086908aefe6167bc8366/Fast-Global-Registration.pdf) [`registration`; ECCV; [Github](https://github.com/intel-isl/FastGlobalRegistration)]
  - [Monocular 3D Object Detection for Autonomous Driving](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Chen_Monocular_3D_Object_CVPR_2016_paper.pdf) [CVPR]
  - [Volumetric and Multi-View CNNs for Object Classification on 3D Data](http://openaccess.thecvf.com/content_cvpr_2016/papers/Qi_Volumetric_and_Multi-View_CVPR_2016_paper.pdf) [CVPR]
  - [Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Ren_Three-Dimensional_Object_Detection_CVPR_2016_paper.pdf) [CVPR]
  - [Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Song_Deep_Sliding_Shapes_CVPR_2016_paper.pdf) [CVPR]
  - [Fpnn: Field probing neural networks for 3d data](http://papers.nips.cc/paper/6416-fpnn-field-probing-neural-networks-for-3d-data.pdf) [NIPS]
  - [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://arxiv.org/pdf/1608.07916) [RSS]
- 2015
  - [Robust Reconstruction of Indoor Scenes](https://www.researchgate.net/profile/Vladlen_Koltun/publication/279751165_Robust_Reconstruction_of_Indoor_Scenes/links/5599867708ae5d8f393633dc/Robust-Reconstruction-of-Indoor-Scenes.pdf) [`reconstruction`; CVPR]
  - [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://arxiv.org/pdf/1605.03344.pdf) [`registration`; TPAMI; [Github](https://github.com/yangjiaolong/Go-ICP)] 
  - [3D ShapeNets: A Deep Representation for Volumetric Shapes](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wu_3D_ShapeNets_A_2015_CVPR_paper.pdf) [CVPR]
  - [SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Song_SUN_RGB-D_A_2015_CVPR_paper.pdf) [CVPR]
  - [Data-Driven 3D Voxel Patterns for Object Category Recognition](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiang_Data-Driven_3D_Voxel_2015_CVPR_paper.pdf) [CVPR]
  - [Multi-view convolutional neural networks for 3d shape recognition](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.pdf) [ICCV]
  - [3d object proposals for accurate object class detection](http://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection.pdf) [NIPS]
  - [Voting for Voting in Online Point Cloud Object](https://pdfs.semanticscholar.org/521f/688f1fadaaff81269040f13270e8ec1a14d4.pdf) [RSS]
  - [Voxnet: A 3d convolutional neural network for real-time object recognition](https://www.ri.cmu.edu/pub_files/2015/9/voxnet_maturana_scherer_iros15.pdf) [IROS]
- 2014
  - [Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Zia_Are_Cars_Just_2014_CVPR_paper.pdf) [CVPR]
  - [Sliding shapes for 3d object detection in depth images](https://pdfs.semanticscholar.org/832e/eaede4f697f005b007798fe0d04829f656b7.pdf) [ECCV]
- 2012
  - [Are we ready for autonomous driving? the kitti vision benchmark suite](http://www.webmail.cvlibs.net/publications/Geiger2012CVPR.pdf) [CVPR]
- 2009
  - [Fast point feature histograms (FPFH) for 3D registration](https://www.cvl.iis.u-tokyo.ac.jp/class2016/2016w/papers/6.3DdataProcessing/Rusu_FPFH_ICRA2009.pdf) [`registration`; ICRA]
  - [Generalized-ICP](http://www.robots.ox.ac.uk/~avsegal/resources/papers/Generalized_ICP.pdf) [`registration`; RSS]
- 1992
  - [A method for registration of 3-D shapes](https://www.researchgate.net/publication/3191994_A_method_for_registration_of_3-D_shapes_IEEE_Trans_Pattern_Anal_Mach_Intell) [`registration`; TPAMI]
- 1987
  - [Least-squares fitting of two 3-D point sets](https://www.researchgate.net/publication/224378053_Least-squares_fitting_of_two_3-D_point_sets_IEEE_T_Pattern_Anal) [`registration`; TPAMI]

## Resources

- [https://github.com/Yochengliu/awesome-point-cloud-analysis](https://github.com/Yochengliu/awesome-point-cloud-analysis)
- [https://github.com/yinyunie/3D-Shape-Analysis-Paper-List](https://github.com/yinyunie/3D-Shape-Analysis-Paper-List)
- [https://github.com/NUAAXQ/awesome-point-cloud-analysis-2020](https://github.com/NUAAXQ/awesome-point-cloud-analysis-2020)
- [https://github.com/QingyongHu/SoTA-Point-Cloud](https://github.com/QingyongHu/SoTA-Point-Cloud)
- [https://github.com/timzhang642/3D-Machine-Learning](https://github.com/timzhang642/3D-Machine-Learning)
- [https://github.com/weiweisun2018/awesome-point-clouds-registration](https://github.com/weiweisun2018/awesome-point-clouds-registration)

## Tools

- [Open3D](http://www.open3d.org/): [https://github.com/intel-isl/Open3D](https://github.com/intel-isl/Open3D)
- [PCL](https://pointclouds.org/): [https://github.com/PointCloudLibrary/pcl](https://github.com/PointCloudLibrary/pcl)
- [PCL-Python](https://python-pcl-fork.readthedocs.io/en/latest/): [https://github.com/strawlab/python-pcl](https://github.com/strawlab/python-pcl)
- [Torch-Points3D](https://arxiv.org/pdf/2010.04642.pdf): [https://github.com/nicolas-chaulet/torch-points3d](https://github.com/nicolas-chaulet/torch-points3d)
- [mmdetection3d](https://mmdetection3d.readthedocs.io/en/latest/): [https://github.com/open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)
- [OpenPCDet](https://github.com/open-mmlab/OpenPCDet): [https://github.com/open-mmlab/OpenPCDet](https://github.com/open-mmlab/OpenPCDet)
- [PyTorch3D](https://arxiv.org/pdf/2007.08501.pdf): [https://github.com/facebookresearch/pytorch3d](https://github.com/facebookresearch/pytorch3d)
- [Minkowski Engine](https://github.com/NVIDIA/MinkowskiEngine): [https://github.com/NVIDIA/MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine)

## Software

- [CloudCompare](https://www.danielgm.net/cc/): [https://github.com/cloudcompare/cloudcompare](https://github.com/cloudcompare/cloudcompare)
- [Semantic Segmentation Editor](https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor): [https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor](https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor)
- [MeshLab](https://www.meshlab.net/): [https://github.com/cnr-isti-vclab/meshlab](https://github.com/cnr-isti-vclab/meshlab)