3D - Point Cloud

Paper list and Datasets about Point Cloud. Datasets can be found in Datasets.md.

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Survey papers

• 3D Semantic Scene Completion: a Survey [arXiv 2021]
• Deep Learning based 3D Segmentation: A Survey [arXiv 2021]
• A comprehensive survey on point cloud registration [arXiv 2021]
• Deep Learning for 3D Point Clouds: A Survey [TPAMI 2020]
• A Comprehensive Performance Evaluation of 3D Local Feature Descriptors [IJCV 2016]

2021

• CVPR
  • Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds [det; Github]
  • RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation [registration]
  • Objects are Different: Flexible Monocular 3D Object Detection [det; Github]
  • FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds [scene flow]
  • HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection [det]
  • Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation [seg; PyTorch]
  • ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning [reg; PyTorch]
  • LiDAR R-CNN: An Efficient and Universal 3D Object Detector [det; Github]
  • Equivariant Point Network for 3D Point Cloud Analysis [Github]
  • PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds [cls, det; Github]
  • Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection [det; Github]
  • Delving into Localization Errors for Monocular 3D Object Detection [det; Github]
  • M3DSSD: Monocular 3D Single Stage Object Detector [det; Github]
  • Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding [completion]
  • Monte Carlo Scene Search for 3D Scene Understanding
  • Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion [seg; Github]
  • PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency [registration; PyTorch]
  • ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection [det; OpenPCDet]
  • Robust Point Cloud Registration Framework Based on Deep Graph Matching [registration; Github]
  • RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction [reconstruction]
  • MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization [motion analysis; Github]
  • TPCN: Temporal Point Cloud Networks for Motion Forecasting [motion forecasting]
  • Self-supervised Geometric Perception [self-supervised; Github]
  • PointGuard: Provably Robust 3D Point Cloud Classification [cls]
  • Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos
  • SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud [det; Github]
  • Center-based 3D Object Detection and Tracking [det, tracking; PyTorch]
  • 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection [det; PyTorch]
  • Style-based Point Generator with Adversarial Rendering for Point Cloud Completion [completion]
  • FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation [pose estimation; Github]
  • Diffusion Probabilistic Models for 3D Point Cloud Generation [generation; Github]
  • GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation [pose estimation; Github]
  • PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers [reconstruction]
  • PREDATOR: Registration of 3D Point Clouds with Low Overlap [registration; PyTorch]
  • SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration [registration; Github]
  • Categorical Depth Distribution Network for Monocular 3D Object Detection [det]
  • Multimodal Motion Prediction with Stacked Transformers [motion prediction; Github]
  • GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection [det; PyTorch]
  • Model-based 3D Hand Reconstruction via Self-Supervised Learning [reconstruction]
  • MonoRUn: Monocular 3D Object Detection by Self-Supervised Reconstruction and Uncertainty Propagation [det; Github]
  • Deep Implicit Moving Least-Squares Functions for 3D Reconstruction [reconstruction; Tensorflow]
  • Skeleton Merger: an Unsupervised Aligned Keypoint Detector [keypoint; PyTorch]
• Others
  • Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network [autonomous driving; NeurIPS]
  • PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds [wireframe; ICLR]
  • Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks [cls, seg; ; ICRA]
  • 3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs [keypoint; Github; ICRA]
  • NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation [localisation; ICRA]
  • Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation [depth estimation; ICRA]
  • YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection [det; PyTorch; ICRA]
  • ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building [static map; ICRA]
  • CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds [pose estimation; Tensorflow; ICRA]
  • PointCutMix: Regularization Strategy for Point Cloud Classification [cls; code; ICML]
  • Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud [seg; Github; AAAI]
  • PointINet: Point Cloud Frame Interpolation Network [frame interpolation; PyTorch; AAAI]
  • Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds [seg; code; AAAI]
  • Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection [det; AAAI]
  • Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [cls, seg; AAAI]
  • CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud [det; PyTorch; AAAI]
  • Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion [seg; Github; AAAI]
  • labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds [labelingg tool; CAD]
• arXiv
  • Geometry-aware data augmentation for monocular 3D object detection [det]
  • OCM3D: Object-Centric Monocular 3D Object Detection [det]
  • Towards Efficient Graph Convolutional Networks for Point Cloud Handling [network; Github]
  • Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds [scene flow]
  • A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds [UDA]
  • SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000× Fewer Labels [seg; Github]
  • View-Guided Point Cloud Completion [completion]
  • One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation [seg]
  • Potential Convolution: Embedding Point Clouds into Potential Fields [cls, seg]
  • Group-Free 3D Object Detection via Transformers [det; PyTorch]
  • 3D-MAN: 3D Multi-frame Attention Network for Object Detection [det]
  • SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud [det; Github]
  • 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning [registration; Github]
  • Multi-view 3D Reconstruction with Transformer [reconstruction]
  • X-view: Non-egocentric Multi-View 3D Object Detector [det]
  • RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation [seg]
  • 3DMNDT: 3D multi-view registration method based on the normal distributions transform [registration]
  • RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection [det]
  • SparsePoint: Fully End-to-End Sparse 3D Object Detector [det]
  • S3Net: 3D LiDAR Sparse Semantic Segmentation Network [seg]
  • Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions [seg]
  • R-PointHop: A Green, Accurate and Unsupervised Point Cloud Registration Method [registration; Github]
  • Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences [autonomous driving; Github]
  • MapFusion: A General Framework for 3D Object Detection with HDMaps [det]
  • Offboard 3D Object Detection from Point Cloud Sequences [det]
  • A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding [det; PyTorch]
  • IRON: Invariant-based Highly Robust Point Cloud Registration [registration]
  • EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation [cls, seg]
  • Pseudo-labeling for Scalable 3D Object Detection [det]
  • LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment [seg]
  • Scalable Scene Flow from Point Clouds in the Real World [scene flow]
  • OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration [registration]
  • InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring [visual grounding]
  • FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation [seg]
  • P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching [matching]
  • UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering [registration; PyTorch]
  • Attention Models for Point Clouds in Deep Learning: A Survey [attention]
  • EfficientLPS: Efficient LiDAR Panoptic Segmentation [seg]
  • HyperPocket: Generative Point Cloud Completion [completion]
  • Point-set Distances for Learning Representations of 3D Point Clouds [representation]
  • DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds [det]
  • PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [det; OpenPCDet]
  • Self-Attention Based Context-Aware 3D Object Detection [det; PyTorch]
  • A two-stage data association approach for 3D Multi-object Tracking [tracking]
  • The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions [seg]
  • Joint Learning of 3D Shape Retrieval and Deformation
  • Self-Supervised Pretraining of 3D Features on any Point-Cloud [det, seg, cls; PyTorch]
  • Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition [place recognition; Tensorflow]

2020

• ECCV
  • PointMixup: Augmentation for point cloud [augmentation, cls; PyTorch]
  • Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations [det; PyTorch]
  • Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets [keypoints]
  • Weakly-supervised 3D Shape Completion in the Wild [completion]
  • SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification [completion, cls; Github]
  • Detail Preserved Point Cloud Completion via Separated Feature Aggregation [completion; Tensorflow]
  • PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds [flow estimation; PyTorch]
  • JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds [seg; Tensorflow]
  • A Closer Look at Local Aggregation Operators in Point Cloud Analysis [cls, seg; Code]
  • Instance-Aware Embedding for Point Cloud Instance Segmentation [seg]
  • Multimodal Shape Completion via Conditional Generative Adversarial Networks [completion; PyTorch]
  • GRNet: Gridding Residual Network for Dense Point Cloud Completion [completion; PyTorch]
  • 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection [det]
  • SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds [det; Github]
  • Pillar-based Object Detection for Autonomous Driving [det, autonomous driving; Tensorflow]
  • EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection [det; PyTorch]
  • Finding Your (3D) Center: 3D Object Detection Using a Learned Loss [det; Tensorflow]
  • Weakly Supervised 3D Object Detection from Lidar Point Cloud [det; PyTorch]
  • H3DNet: 3D Object Detection Using Hybrid Geometric Primitives [det; Tensorflow]
  • Generative Sparse Detection Networks for 3D Single-shot Object Detection [det; Github]
  • Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution [seg, det; PyTorch]
  • DeepGMR: Learning Latent Gaussian Mixture Models for Registration [registration; PyTorch]
  • Quaternion Equivariant Capsule Networks for 3D Point Clouds [PyTorch]
  • PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding [unsupervised; cls, seg, det; PyTorch]
  • Convolutional Occupancy Networks [reconstruction; PyTorch]
  • Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration [registration; PyTorch]
  • Progressive Point Cloud Deconvolution Generation Network [generation; github]
  • Reinforced Axial Refinement Network for Monocular 3D Object Detection [det, monocular]
  • Monocular 3D Object Detection via Feature Domain Adaptation [det, monocular]
  • Improving 3D Object Detection through Progressive Population Based Augmentation [det]
  • An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds [det]
  • Rotation-robust Intersection over Union for 3D Object Detection
• CVPR
  • PointPainting: Sequential Fusion for 3D Object Detection [det]
  • 3DSSD: Point-based 3D Single Stage Object Detector [det; Tensorflow]
  • A Hierarchical Graph Network for 3D Object Detection on Point Clouds [det]
  • Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence [correspondences; Tensorflow]
  • Deep Global Registration [registration; PyTorch]
  • 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation [seg; Github]
  • PointGMM: a Neural GMM Network for Point Clouds [generation, registration; PyTorch]
  • Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [det; Tensorflow]
  • ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes [det]
  • OccuSeg: Occupancy-aware 3D Instance Segmentation [seg]
  • Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation [seg; PyTorch]
  • MLCVNet: Multi-Level Context VoteNet for 3D Object Detection [det; PyTorch]
  • Going Deeper with Lean Point Networks [seg; PyTorch]
  • Point Cloud Completion by Skip-attention Network with Hierarchical Folding [completion]
  • Unsupervised Learning of Intrinsic Structural Representation Points [PyTorch]
  • PF-Net: Point Fractal Network for 3D Point Cloud Completion [completion; PyTorch]
  • PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection [det; code]
  • Adaptive Hierarchical Down-Sampling for Point Cloud Classification [downsampling, cls]
  • SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud [det; PyTorch]
  • 3DRegNet: A Deep Neural Network for 3D Point Registration [registration; Tensorflow]
  • MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment [non-rigid alignment]
  • SampleNet: Differentiable Point Cloud Sampling [sample, cls, registration, reconstruction; PyTorch]
  • Learning multiview 3D point cloud registration [multiview registration; PyTorch]
  • Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences [registration; PyTorch]
  • PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling [cls, seg; Tensorflow]
  • Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [unsupervised; cls; PyTorch]
  • Grid-GCN for Fast and Scalable Point Cloud Learning [cls, seg; mxnet]
  • FPConv: Learning Local Flattening for Point Convolution [cls, seg; PyTorch]
  • PointAugment: an Auto-Augmentation Framework for Point Cloud Classification [cls, retrieval; github]
  • RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds [seg; Tensorflow]
  • Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels [weakly supervised; seg; Tensorflow]
  • PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation [seg; PyTorch]
  • Learning to Segment 3D Point Clouds in 2D Image Space [seg; Keras]
  • PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation [seg; PyTorch]
  • D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [keypoints, registration; Tensorflow, PyTorch]
  • RPM-Net: Robust Point Matching using Learned Features [registration; PyTorch]
  • Cascaded Refinement Network for Point Cloud Completion [completion; Tensorflow]
  • P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds [tracking; PyTorch]
  • An Efficient PointLSTM for Point Clouds Based Gesture Recognition [gesture; PyTorch]
• Others
  • Group Contextual Encoding for 3D Point Clouds [det, cls; PyTorch; NeurIPS]
  • CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations [dynamic sequences; Github; NeurIPS]
  • Skeleton-bridged Point Completion: From Global Inference to Local Adjustment [completion; NeurIPS]
  • Self-Supervised Few-Shot Learning on Point Clouds [cls, seg; NeurIPS]
  • Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud [cls; NeurIPS]
  • PIE-NET: Parametric Inference of Point Cloud Edges [edge det; NeurIPS]
  • Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds [cls, seg; Tensorflow; TPAMI]
  • From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network [det; PyTorch; TPAMI]
  • Unpaired Point Cloud Completion on Real Scans using Adversarial Training [completion; Tensorflow; ICLR]
  • AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing [cls, seg; PyTorch; ICLR]
  • Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds [ICLR]
  • MSN: Morphing and Sampling Network for Dense Point Cloud Completion [completion; PyTorch; AAAI]
  • TANet: Robust 3D Object Detection from Point Clouds with Triple Attention [det; PyTorch; AAAI]
  • JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds [seg; Tensorflow]
  • Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling [cls, seg; AAAI]
  • Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution [cls, seg, matching; AAAI]
  • Differentiable Manifold Reconstruction for Point Cloud Denoising [denoising; PyTorch; ACM MM]
  • Weakly Supervised 3D Object Detection from Point Clouds [det; Tensorflow; ACM MM]
  • Unsupervised Detection of Distinctive Regions on 3D Shapes [unsupervised; Tensorflow; TOG]
  • Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds [seg, cls; Project; ICRA]
  • Semantic Graph Based Place Recognition for 3D Point Clouds [place recognition; PyTorch; IROS]
  • End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences [registration; PyTorch; IROS]
  • Correspondence Matrices are Underrated [registration, correspondence; PyTorch; 3DV]
  • Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks [cls, seg; 3DV]
  • PanoNet3D: Combining Semantic and Geometric Understanding for LiDAR Point Cloud Detection [det; 3DV]
  • FKAConv: Feature-Kernel Alignment for Point Cloud Convolution [conv, cls, seg; PyTorch; ACCV]
  • Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks [conv, cls; ACCV]
  • Reconstructing Human Body Mesh from Point Clouds by Adversarial GP Network [reconstruction; ACCV]
  • Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds [seg; Tensorflow; ACCV]
  • SDP-Net: Scene Flow Based Real-time Object Detection and Prediction from Sequential 3D Point Clouds [det; ACCV]
  • Best Buddies Registration for Point Clouds [registration; PyTorch; ACCV]
  • HPGCNN: Hierarchical Parallel Group Convolutional Neural Networks for Point Clouds Processing [conv, cls, seg; ACCV]
  • SAUM: Symmetry-Aware Upsampling Module for Consistent Point Cloud Completion [completion; Tensorflow; ACCV]
  • Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features [registration; Remote Sensing]
  • ConvPoint: Continuous Convolutions for Point Cloud Processing [cls, seg; PyTorch; Computers & Graphics]
• arXiv
  • Multi-Modality Cut and Paste for 3D Object Detection [det; PyTorch]
  • Self-Supervised Learning for Domain Adaptation on Point Clouds [cls, seg]
  • SALA: Soft Assignment Local Aggregation for 3D Semantic Segmentation [seg]
  • CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds [correspondence]
  • Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation [seg]
  • Geometric robust descriptor for 3D point cloud [registration, cls, seg]
  • PCT: Point Cloud Transformer [cls, seg, normal estimation; Jittor]
  • Point Transformer(Hengshuang Zhao) [seg, cls; PyTorch-unofficial]
  • Point Transformer(Nico) [cls, seg]
  • Deterministic PointNetLK for Generalized Registration [registration]
  • Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation [seg; PyTorch]
  • OcCo: Pre-Training by Completing Point Clouds [pre-training, completion; Github]
  • Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration [registration]
  • PRE-TRAINING BY COMPLETING POINT CLOUDS [pre-training, cls, seg; Github]
  • Continuous Geodesic Convolutions for Learning on 3D Shapes [descriptor, match, seg]
  • Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation [seg]
  • A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds [det]
  • TEASER: Fast and Certifiable Point Cloud Registration [registration; Github]
  • Part-Aware Data Augmentation for 3D Object Detection in Point Cloud [det, augmentation; PyTorch]

2019

• ICCV
  • Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning [denoising; Tensorflow]
  • 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions [generation; PyTorch]
  • STD: Sparse-to-Dense 3D Object Detector for Point Cloud [det]
  • USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds [keypoints, registration; PyTorch]
  • LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis [place recognition]
  • Unsupervised Multi-Task Feature Learning on Point Clouds [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 [unsupervised, cls, generation, seg, completion]
  • SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences [dataset]
  • MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences [cls, seg, flow estimation; Tensorflow]
  • DeepGCNs: Can GCNs Go as Deep as CNNs? [seg; Tensorflow]
  • VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation [seg; Github]
  • Interpolated Convolutional Networks for 3D Point Cloud Understanding [cls, seg]
  • Dynamic Points Agglomeration for Hierarchical Point Sets Learning [cls, seg]
  • ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics [cls, seg; Tensorflow]
  • Fast Point R-CNN [det]
  • Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data [dataset; cls; Tensorflow]
  • KPConv: Flexible and Deformable Convolution for Point Clouds [cls, seg; code]
  • Fully Convolutional Geometric Features [match; PyTorch]
  • Deep Closest Point: Learning Representations for Point Cloud Registration [registration; PyTorch]
  • DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration [registration]
  • Efficient and Robust Registration on the 3D Special Euclidean Group [registration]
  • Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation [seg]
  • DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing [cls, retrieval, seg, normal estimation; PyTorch]
  • DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense [cls]
  • Efficient Learning on Point Clouds with Basis Point Sets [cls, registration; PyTorch]
  • PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows [generation, reconstruction; Pytorch
  • PU-GAN: a Point Cloud Upsampling Adversarial Network [upsampling, reconstruction; Project]
  • 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition [retrieval, place recognition]
  • Deep Hough Voting for 3D Object Detection in Point Clouds [det; PyTorch]
  • Exploring the Limitations of Behavior Cloning for Autonomous Driving [autonomous driving; Pytorch]
• CVPR
  • LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [det; Github]
  • TopNet: Structural Point Cloud Decoder [completion; Github]
  • FlowNet3D: Learning Scene Flow in 3D Point Clouds [scene flow; Tensorflow]
  • Occupancy Networks: Learning 3D Reconstruction in Function Space [reconstruction]
  • Associatively Segmenting Instances and Semantics in Point Clouds [seg; Tensorflow]
  • 3D Point Capsule Networks [autoencoder; PyTorch]
  • Patch-based Progressive 3D Point Set Upsampling [upsampling; Tensorflow, PyTorch]
  • Generating 3D Adversarial Point Clouds [adversary; Tensorflow]
  • RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion [completion; PyTorch]
  • GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud [seg; Tensorflow]
  • JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields [seg; PyTorch]
  • 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans [seg; PyTorch]
  • Learning Transformation Synchronization [transformation synchronization, registration; PyTorch]
  • SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences [registration; Github]
  • Learning Transformation Synchronization [reconstruction; PyTorch]
  • 3D Local Features for Direct Pairwise Registration [registration]
  • DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds [registration; Github]
  • Relation-Shape Convolutional Neural Network for Point Cloud Analysis [cls, seg, normal estimation; PyTorch]
  • Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN [cls, det; Tensorflow]
  • 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks [seg; PyTorch]
  • PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval [retrieval; Tensorflow]
  • Attentional PointNet for 3D-Object Detection in Point Clouds [det; PyTorch]
  • Octree guided CNN with Spherical Kernels for 3D Point Clouds [cls, seg; Github]
  • A-CNN: Annularly Convolutional Neural Networks on Point Clouds [cls, seg; Tensorflow]
  • ClusterNet: Deep Hierarchical Cluster Network with Rigorously Rotation-Invariant Representation for Point Cloud Analysis [cls]
  • Graph Attention Convolution for Point Cloud Semantic Segmentation [seg; PyTorch-unofficial]
  • PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing [seg, cls; PyTorch]
  • Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling [cls, seg, gesture]
  • Learning to Sample [sample, cls, retrieval, reconstruction; Tensorflow]
  • PointConv: Deep Convolutional Networks on 3D Point Clouds [cls, seg; Tensorflow]
  • The Perfect Match: 3D Point Cloud Matching With Smoothed Densities [match; code]
  • PointNetLK: Point Cloud Registration using PointNet [registration; PyTorch]
  • PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud [det; PyTorch]
  • PointPillars: Fast Encoders for Object Detection From Point Clouds [det; Pytorch]
  • Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [depth estimation, det; github]
  • ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving [dataset, autonomous driving]
  • Stereo R-CNN based 3D Object Detection for Autonomous Driving [det, autonomous driving; github]
  • Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [det, autonomous driving; Tesorflow]
  • LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [det]
  • GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving [det, autonomous driving]
  • L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving [autonomous driving]
  • Iterative Transformer Network for 3D Point Cloud [pose, cls, seg; Tensorflow]
• Others
  • PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation [domain adaptation; PyTorch; NeurIPS]
  • Learning elementary structures for 3D shape generation and matching [generation, matching; NeurIPS]
  • Self-Supervised Deep Learning on Point Clouds by Reconstructing Space [self-supervised, cls, seg; NeurIPS]
  • Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds [seg; Tensorflow; NeurIPS]
  • PRNet: Self-Supervised Learning for Partial-to-Partial Registration [registration, cls; PyTorch; NeurIPS]
  • Point-Voxel CNN for Efficient 3D Deep Learning [seg, det; PyTorch; NeurIPS]
  • L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention [autoencoder; ACM MM]
  • Deep Cascade Generation on Point Sets [generation; PyTorch; IJCAI]
  • A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates [registration; RSS]
  • Dynamic Graph CNN for Learning on Point Clouds [cls, seg; Github; TOG]
  • SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud [seg; Tensorflow; ICRA]
  • RangeNet++: Fast and Accurate LiDAR Semantic Segmentation [seg; PyTorch; IROS]
  • AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects [registration; Tensorflow; 3DV]
  • Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network [reconstruction; WACV]
• arXiv
  • Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection [det]
  • PCRNet: Point Cloud Registration Network using PointNet Encoding [registration; PyTorch, Tensorflow]
  • LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer [cls, seg; Tensorflow]
  • Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving [autonomous driving]
  • Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features [cls, seg; Tensorflow]

2018

• CVPR
  • Learning 3D Shape Completion From Laser Scan Data With Weak Supervision [completion; Github]
  • Deep Parametric Continuous Convolutional Neural Networks [seg, motion estimation(lidar flow)]
  • Attentional ShapeContextNet for Point Cloud Recognition [cls, seg]
  • A Papier-Mâché Approach to Learning 3D Surface Generation [generation; PyTorch]
  • Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs [seg; PyTorch]
  • FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation [autoencoder, unsupervised; code]
  • FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis [correspondence, seg; Tensorflow]
  • PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition [retrieval, place recognition; Tensorflow]
  • PU-Net: Point Cloud Upsampling Network [upsampling; Tensorflow]
  • SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation [seg; Tensorflow]
  • Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling [cls, seg; code]
  • Tangent Convolutions for Dense Prediction in 3D [seg; Tensorflow]
  • PointGrid: A Deep Network for 3D Shape Understanding [cls, seg; Tensorflow]
  • 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks [seg; Github]
  • Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs [seg; PyTorch]
  • SPLATNet: Sparse Lattice Networks for Point Cloud Processing [seg; Caffe]
  • Pointwise Convolutional Neural Networks [cls, seg; Tensorflow]
  • SO-Net: Self-Organizing Network for Point Cloud Analysis [autoencoder, cls, seg; PyTorch]
  • Recurrent Slice Networks for 3D Segmentation of Point Clouds [seg; PyTorch]
  • PPFNet: Global Context Aware Local Features for Robust 3D Point Matching [registration]
  • PIXOR: Real-Time 3D Object Detection From Point Clouds [det; PyTorch]
  • Frustum PointNets for 3D Object Detection From RGB-D Data [det; Tensorflow]
  • VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [det]
  • 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare [reconstruction]
  • Multi-Level Fusion Based 3D Object Detection From Monocular Images [det]
• ECCV
  • 3D-CODED : 3D Correspondences by Deep Deformation [matching; PyTorch]
  • SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters [cls, seg; Tensorflow]
  • 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues [seg, cls]
  • Multiresolution Tree Networks for 3D Point Cloud Processing [cls, generation; PyTorch]
  • HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration [registration; unofficial code]
  • EC-Net: an Edge-aware Point set Consolidation Network [consolidation; Tensorflow]
  • Learning and Matching Multi-View Descriptors for Registration of Point Clouds [registration]
  • Local Spectral Graph Convolution for Point Set Feature Learning [cls, seg]
  • 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation [seg]
  • Fully-Convolutional Point Networks for Large-Scale Point Clouds [seg, captioning; Tensorflow]
  • PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors [registration; PyTorch-unofficial]
  • Deep Continuous Fusion for Multi-Sensor 3D Object Detection [det]
  • 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [match, registration; Tensorflow]
  • Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving [autonomous driving]
• Others
  • PointCNN: Convolution On X -Transformed Points [cls, seg; Tensorflow; NeurIPS]
  • Learning Representations and Generative Models for 3D Point Clouds [autoencoder; Tensorflow; ICML]
  • RGCNN: Regularized Graph CNN for Point Cloud Segmentation [seg, cls; Tensorflow; ACM MM]
  • PCN: Point Completion Network [completion; Tensorflow; 3DV]
  • Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration [registration; 3DV]
  • Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods [seg; 3DV]
  • Guaranteed Outlier Removal for Point Cloud Registration with Correspondences [registration; TPAMI]
  • Second: Sparsely embedded convolutional detection [det; Sensors]
  • Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving [det, autonomous driving; IEEE Robotics and Automation Letters]
  • HDNET: Exploiting HD Maps for 3D Object Detection [det, autonomous driving; CoRL]
  • Joint 3D Proposal Generation and Object Detection from View Aggregation [det, autonomous driving; IROS]
  • Flex-Convolution(Million-Scale Point-Cloud Learning Beyond Grid-Worlds) [cls, seg; Tensorflow; ACCV]
  • SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud [seg; Tensorflow; ICRA]
  • Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds [seg, cls, normal estimation; Tensorflow; TOG]
  • Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction [reconstruction; Tensorflow; AAAI]
• arXiv
  • Spherical Convolutional Neural Network for 3D Point Clouds [cls]
  • Point Convolutional Neural Networks by Extension Operators [cls, seg, normal estimation; Tensorflow]
  • PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation [seg; Tensorflow]
  • Point Cloud GAN [generation; PyTorch]
  • Roarnet: A robust 3d object detection based on region approximation refinement [det]
  • Complex-YOLO: Real-time 3D Object Detection on Point Clouds [det; PyTorch]
  • Classification of Point Cloud Scenes with Multiscale Voxel Deep Network [seg]

2017

• CVPR
  • Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis [completion; Torch7]
  • SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation [seg, keypoints; Github]
  • A Point Set Generation Network for 3D Object Reconstruction From a Single Image [reconstruction; Tensorflow]
  • Multi-View 3D Object Detection Network for Autonomous Driving [det, autonomous driving; Tensorflow]
  • Deep MANTA: A Coarse-To-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis From Monocular Image [autonomous driving]
  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [cls, seg; Tensorflow]
  • 3D Bounding Box Estimation Using Deep Learning and Geometry [det]
  • OctNet: Learning Deep 3D Representations at High Resolutions [cls, seg, orientation estimation; PyTorch]
  • 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions [match, registration; project]
  • 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder [registration; github]
• ICCV
  • High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference [completion]
  • Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models [cls, retrieval, seg; PyTorch-unofficial]
  • Learning Compact Geometric Features [registration; Github]
  • 2D-Driven 3D Object Detection in RGB-D Images [det]
• Others
  • Pointnet++: Deep hierarchical feature learning on point sets in a metric space [cls, seg; Tensorflow; NIPS]
  • Deep Sets [PyTorch; cls]
  • 3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection [det, autonomous driving; TPAMI]
  • O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis [cls, retrieval, seg; Github; TOG]
  • Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks [det; ICRA]
  • 3d fully convolutional network for vehicle detection in point cloud [det; Tensorflow; IROS]
  • Shape Completion Enabled Robotic Grasping [completion; Keras; IROS]
  • SEGCloud: Semantic Segmentation of 3D Point Clouds [seg; 3DV]

Before 2016

• 2016
  • Fast Global Registration [registration; ECCV; Github]
  • Monocular 3D Object Detection for Autonomous Driving [CVPR]
  • Volumetric and Multi-View CNNs for Object Classification on 3D Data [CVPR]
  • Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients [CVPR]
  • Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images [CVPR]
  • Fpnn: Field probing neural networks for 3d data [NIPS]
  • Vehicle Detection from 3D Lidar Using Fully Convolutional Network [RSS]
• 2015
  • Robust Reconstruction of Indoor Scenes [reconstruction; CVPR]
  • Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration [registration; TPAMI; Github]
  • 3D ShapeNets: A Deep Representation for Volumetric Shapes [CVPR]
  • SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite [CVPR]
  • Data-Driven 3D Voxel Patterns for Object Category Recognition [CVPR]
  • Multi-view convolutional neural networks for 3d shape recognition [ICCV]
  • 3d object proposals for accurate object class detection [NIPS]
  • Voting for Voting in Online Point Cloud Object [RSS]
  • Voxnet: A 3d convolutional neural network for real-time object recognition [IROS]
• 2014
  • Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects [CVPR]
  • Sliding shapes for 3d object detection in depth images [ECCV]
• 2012
  • Are we ready for autonomous driving? the kitti vision benchmark suite [CVPR]
• 2009
  • Fast point feature histograms (FPFH) for 3D registration [registration; ICRA]
  • Generalized-ICP [registration; RSS]
• 1992
  • A method for registration of 3-D shapes [registration; TPAMI]
• 1987
  • Least-squares fitting of two 3-D point sets [registration; TPAMI]

Resources

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

Tools

• Open3D: https://github.com/intel-isl/Open3D
• PCL: https://github.com/PointCloudLibrary/pcl
• PCL-Python: https://github.com/strawlab/python-pcl
• Torch-Points3D: https://github.com/nicolas-chaulet/torch-points3d
• mmdetection3d: https://github.com/open-mmlab/mmdetection3d
• OpenPCDet: https://github.com/open-mmlab/OpenPCDet
• PyTorch3D: https://github.com/facebookresearch/pytorch3d
• Minkowski Engine: https://github.com/NVIDIA/MinkowskiEngine

Software

• CloudCompare: https://github.com/cloudcompare/cloudcompare
• Semantic Segmentation Editor: https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor
• MeshLab: https://github.com/cnr-isti-vclab/meshlab