Paper list and Datasets about Point Cloud. Datasets can be found in Datasets.md.
- 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]
- CVPR
- Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning [
seg
] - Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts [
seg
; PyTorch] - PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks [
upsampling
; Tensorflow] - Self-Point-Flow: Self-Supervised Scene Flow Estimation from Points Clouds with Optimal Transport and Random Walk [
scene flow
] - SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data [
reconstruction
] - HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding [
scene flow
] - 3D Spatial Recognition without Spatially Labeled 3D [
det
,seg
] - LASR: Learning Articulated Shape Reconstruction from a Monocular Video [
reconstruction
,monocular
] - VoxelContext-Net: An Octree based Framework for Point Cloud Compression [
compression
] - Unsupervised 3D Shape Completion through GAN Inversion [
completion
; PyTorch] - KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control [Github]
- Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [
autonomous driving
; PyTorch] - Self-Supervised Pillar Motion Learning for Autonomous Driving [
autonomous driving
; Github] - Variational Relational Point Completion Network [
completion
] - 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]
- Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning [
- Others
- GIDSeg: Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors [
seg
; RAL] - PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery [
sampling
; Github; IJCAI] - Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective [
completion
; TOG] - Fast and Robust Iterative Closest Point [
registration
; Github; TPAMI] - MonoGRNet: A General Framework for Monocular 3D Object Detection [
monocular
,det
; TPAMI] - 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] - Self-Guided Instance-Aware Network for Depth Completion and Enhancement [
depth
; ICRA] - FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection [
det
; Github; ICRA] - 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 [
labeling tool
; CAD]
- GIDSeg: Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors [
- arXiv
- TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields [
place recognition
] - Generalisable and distinctive 3D local
deep descriptors for point cloud registration [
registration
] - Deep Weighted Consensus (DWC) Dense correspondence confidence maps for 3D shape registration [
registration
] - M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers [
det
] - FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [
monocular
,det
; mmdet3D] - Boundary-Aware 3D Object Detection from Point Clouds [
det
] - Lidar Point Cloud Guided Monocular 3D Object Detection [
det
] - 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]
- TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields [
- 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
- DPDist: Comparing Point Clouds Using Deep Point Cloud Distance
- PointMixup: Augmentation for point cloud [
- 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]
- PointPainting: Sequential Fusion for 3D Object Detection [
- 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]
- Group Contextual Encoding for 3D Point Clouds [
- 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]
- Multi-Modality Cut and Paste for 3D Object Detection [
- 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]
- Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning [
- 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]
- LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [
- 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]
- PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation [
- 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]
- Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection [
- 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
]
- Learning 3D Shape Completion From Laser Scan Data With Weak Supervision [
- 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
]
- 3D-CODED : 3D Correspondences by Deep Deformation [
- 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]
- PointCNN: Convolution On X -Transformed Points [
- 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
]
- Spherical Convolutional Neural Network
for 3D Point Clouds [
- 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]
- Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis [
- 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
]
- High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference [
- 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]
- Pointnet++: Deep hierarchical feature learning on point sets in a metric space [
- 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]
- Fast Global Registration [
- 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]
- Robust Reconstruction of Indoor Scenes [
- 2014
- 2012
- 2009
- Fast point feature histograms (FPFH) for 3D registration [
registration
; ICRA] - Generalized-ICP [
registration
; RSS]
- Fast point feature histograms (FPFH) for 3D registration [
- 1992
- A method for registration of 3-D shapes [
registration
; TPAMI]
- A method for registration of 3-D shapes [
- 1987
- Least-squares fitting of two 3-D point sets [
registration
; TPAMI]
- Least-squares fitting of two 3-D point sets [
- 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
- 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