3D - Point Cloud

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

Survey papers

Transformers in 3D Point Clouds: A Survey [arXiv 2022]
Surface Reconstruction from Point Clouds: A Survey and a Benchmark [arXiv 2022]
Sequential Point Clouds: A Survey [arXiv 2022]
A Survey of Robust LiDAR-based 3D Object Detection Methods for Autonomous Driving [arXiv 2022]
A Survey of Non-Rigid 3D Registration [Eurographics 2022]
Comprehensive Review of Deep Learning-Based 3D Point Clouds Completion Processing and Analysis [arXiv 2022]
Unsupervised Representation Learning for Point Clouds: A Survey [arXiv 2022]
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey [arXiv 2022]
3D Object Detection from Images for Autonomous Driving: A Survey [arXiv 2022]
Survey and Systematization of 3D Object Detection Models and Methods [arXiv 2022]
3D Object Detection for Autonomous Driving: A Survey [arXiv 2021]
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey [arXiv 2021]
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]

2022

CVPR
- MonoGround: Detecting Monocular 3D Objects from the Ground [det, monocular; Github]
- PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos [reconstruction; PyTorch]
- Learning 3D Object Shape and Layout without 3D Supervision [shape, layout; Project]
- Deterministic Point Cloud Registration via Novel Transformation Decomposition [registration]
- Cross-view Transformers for real-time Map-view Semantic Segmentation [autonomous driving; PyTorch]
- RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding [compression]
- Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation [seg; Github]
- Voxel Field Fusion for 3D Object Detection [det; PyTorch]
- On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models [autonomous driving; CVPRW]
- 3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies [reconstruction]
- The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution [cls, seg]
- Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors [shape mating; Project]
- Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving [autonomous driving, monocular]
- SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation [seg; PyTorch]
- Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives [cls]
- RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds [scene flow; Github]
- Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection [det]
- Surface Representation for Point Clouds [cls, seg, det; PyTorch]
- FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction [reconstruction; PyTorch]
- Topologically-Aware Deformation Fields for Single-View 3D Reconstruction [reconstruction; Project]
- Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching [matching; PyTorch]
- Rotationally Equivariant 3D Object Detection [det; Project]
- MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation [seg; Project]
- Density-preserving Deep Point Cloud Compression [compression; PyTorch]
- Coupled Iterative Refinement for 6D Multi-Object Pose Estimation [pose estimation; PyTorch]
- A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching [match; Github]
- Focal Sparse Convolutional Networks for 3D Object Detection [det; PyTorch]
- Surface Reconstruction from Point Clouds by Learning Predictive Context Priors [reconstruction; Tensorflow]
- Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors [reconstruction; Tensorflow]
- Forecasting from LiDAR via Future Object Detection [forecasting; PyTorch]
- Fast Point Transformer [seg, det; PyTorch]
- Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity [seg; CVPRW]
- Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles [autonomous driving; CVPRW]
- Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation [seg; PyTorch]
- OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data [det; PyTorch]
- 3DeformRS: Certifying Spatial Deformations on Point Clouds [robustness; Github]
- HyperDet3D: Learning a Scene-conditioned 3D Object Detector [det]
- Exploiting Temporal Relations on Radar Perception for Autonomous Driving [autonomous driving]
- Homography Loss for Monocular 3D Object Detection [det]
- CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection [det]
- Learning to Detect Mobile Objects from LiDAR Scans Without Labels [det; PyTorch]
- Learning Local Displacements for Point Cloud Completion [completion]
- Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds [scene flow; Github]
- LiDAR Snowfall Simulation for Robust 3D Object Detection [det; Github]
- Text2Pos: Text-to-Point-Cloud Cross-Modal Localization [localization; PyTorch]
- Stratified Transformer for 3D Point Cloud Segmentation [seg; PyTorch]
- REGTR: End-to-end Point Cloud Correspondences with Transformers [registration; PyTorch]
- Equivariant Point Cloud Analysis via Learning Orientations for Message Passing [cls, seg, normal estimation; Github]
- SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration [registration; Github]
- Towards Implicit Text-Guided 3D Shape Generation [generation; PyTorch]
- Point2Seq: Detecting 3D Objects as Sequences [det; PyTorch]
- MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection [det, monocular; Github]
- AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception [det, seg; Github]
- IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment [interpolation; Github]
- TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers [det; PyTorch]
- Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds [det; PyTorch]
- No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces [cls; Github]
- MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer [det, monocular; Github]
- Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds [det; PyTorch]
- VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention [det; PyTorch]
- Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion [det]
- AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation [completion, reconstruction, generation]
- DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection [det; Tensorflow]
- Scribble-Supervised LiDAR Semantic Segmentation [seg; Github]
- MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection [det, monocular; Github]
- PTTR: Relational 3D Point Cloud Object Tracking with Transformer [tracking; PyTorch]
- AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation [seg]
- Point Density-Aware Voxels for LiDAR 3D Object Detection [det; Github]
- Contrastive Boundary Learning for Point Cloud Segmentation [seg; Github]
- Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement [det; PyTorch]
- Shape-invariant 3D Adversarial Point Clouds [adversarial; Github]
- Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects [tracking; Github]
- ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation [cls; Github]
- Geometric Transformer for Fast and Robust Point Cloud Registration [registration; PyTorch]
- Lepard: Learning partial point cloud matching in rigid and deformable scenes [registration; PyTorch]
- Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving [det, monocular; Github]
- A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation [det]
- Embracing Single Stride 3D Object Detector with Sparse Transformer [det; PyTorch]
- Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes [det; PyTorch]
- CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [cross-modal learning; PyTorch]
- PointCLIP: Point Cloud Understanding by CLIP [cross-modal learning; Github]
- SoftGroup for 3D Instance Segmentation on Point Clouds [seg; PyTorch]
- Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds [tracking; PyTorch]
- A Unified Query-based Paradigm for Point Cloud Understanding [det, seg, cls]
AAAI
Others
- Benchmarking and Analyzing Point Cloud Classification under Corruptions [cls; PyTorch; ICML]
- BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation [pose estimation; IJCAI]
- Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds [captioning; Github; IJCAI]
- PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution [seg, det; TPAMI]
- Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images [generation, mesh; TPAMI]
- Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds [normal refinement; PyTorch; TPAMI]
- PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths [completion; PyTorch; TPAMI]
- WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration [registration; PyTorch; TVCG]
- Point Set Self-Embedding [embedding; Github; TVCG]
- SoftPool++: An Encoder-Decoder Network for Point Cloud Completion [completion; IJCV]
- RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning [cls, seg, retrieval; IJCV]
- Enhanced Prototypical Learning for Unsupervised Domain Adaptation in LiDAR Semantic Segmentation [seg; ICRA]
- Multi-Class 3D Object Detection with Single-Class Supervision [det; ICRA]
- Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions [pose estimation; Project; ICRA]
- HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud [place recognition; ICRA]
- RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds [scene flow; ICRA]
- Variable Rate Compression for Raw 3D Point Clouds [compression; Github; ICRA]
- Hindsight is 2020: Leveraging Past Traversals to Aid 3D Perception [det; PyTorch; ICLR]
- WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection [det, monocular; Github; ICLR]
- Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration [non-rigid, registration; ICLR]
- A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [completion; PyTorch; ICLR]
- Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework [cls, seg; PyTorch; ICLR]
- MonoDistill: Learning Spatial Features for Monocular 3D Object Detection [det, monocular; Github; ICLR]
- urban_road_filter: A real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles [det, autonomous driving; Github; Video; Sensors]
- Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions [seg; Github; RAL]
- Temporal Point Cloud Completion with Pose Disturbance [completion; RAL]
- Semi-supervised 3D shape segmentation with multilevel consistency and part substitution [seg; Tensorflow; CVM]
- Point cloud completion on structured feature map with feedback network [completion; CVM]
- TorchSparse: Efficient Point Cloud Inference Engine [engine; PyTorch; MLSys]
arXiv
- A Simple Baseline for BEV Perception Without LiDAR [autonomous driving; Project]
- Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot [autonomous driving; Github]
- Online Segmentation of LiDAR Sequences: Dataset and Algorithm [seg; Project]
- K-Radar: 4D Radar Object Detection Dataset and Benchmark for Autonomous Driving in Various Weather Conditions [autonomous driving; Github]
- LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection [det]
- Semi-signed neural fitting for surface reconstruction from unoriented point clouds [reconstruction]
- SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse views [reconstruction; Github]
- PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories [completion; Project]
- PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies [seg, cls; Github]
- AGConv: Adaptive Graph Convolution on 3D Point Clouds [cls, seg; PyTorch]
- VN-Transformer: Rotation-Equivariant Attention for Vector Neurons [cls, motion forecasting]
- Learning Ego 3D Representation as Ray Tracing [autonomous driving; Github]
- Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking [det, tracking]
- SHRED: 3D Shape Region Decomposition with Learned Local Operations [decomposition]
- Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization [registration, non-rigid; Github]
- GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions [representation; Github]
- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images [autonomous driving]
- SNAKE: Shape-aware Neural 3D Keypoint Field [keypoints; PyTorch]
- Semantic Instance Segmentation of 3D Scenes Through Weak Bounding Box Supervision [seg; Project]
- SparseDet: Towards End-to-End 3D Object Detection [det]
- Unifying Voxel-based Representation with Transformer for 3D Object Detection [det; PyTorch]
- TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [autonomous driving; PyTorch]
- Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training [self-supervised; Github]
- Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [det; Github]
- Towards Efficient 3D Object Detection with Knowledge Distillation [det]
- OpenCalib: A multi-sensor calibration toolbox for autonomous driving [calibration, autonomous driving; Github]
- BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework [det; PyTorch]
- Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [det]
- BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [autonomous driving; Project]
- Non-rigid Point Cloud Registration with Neural Deformation Pyramid [non-rigid, registration; PyTorch]
- Robust 3D Object Detection in Cold Weather Conditions [det]
- PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection [det; Github]
- BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving [autonomous driving; Github]
- Continual learning on 3D point clouds with random compressed rehearsal [continual learning]
- Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap [seg]
- A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration [registration; Github]
- PillarNet: High-Performance Pillar-based 3D Object Detection [det]
- MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection [det]
- Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning [seg]
- Cost-Aware Comparison of LiDAR-based 3D Object Detectors [det]
- Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [cls, seg]
- APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification [cls]
- Point Cloud Compression with Sibling Context and Surface Priors [compression]
- PointInst3D: Segmenting 3D Instances by Points [seg]
- Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training [det, monocular]
- Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection [det]
- CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation [seg]
- RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds [reconstruction; Github]
- Dynamic Point Cloud Denoising via Gradient Fields [denoising]
- Stress-Testing LiDAR Registration [registration; Github]
- Language-Grounded Indoor 3D Semantic Segmentation in the Wild [seg; Project]
- GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation [autonomous driving]
- M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation [det, seg; Project]
- DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors [det; Github]
- RBGNet: Ray-based Grouping for 3D Object Detection [det; Github]
- POS-BERT: Point Cloud One-Stage BERT Pre-Training [cls, seg; Github]
- DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation [seg]
- BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers [det, seg; Github]
- Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds [sampling]
- ImpDet: Exploring Implicit Fields for 3D Object Detection [det]
- Learning a Structured Latent Space for Unsupervised Point Cloud Completion [completion]
- Self-Supervised Point Cloud Representation Learning with Occlusion Auto-Encoder [self-supervised; Github]
- MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation [seg]
- LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection [det; PyTorch]
- Towards 3D Scene Understanding by Referring Synthetic Models [transfer learning]
- Unsupervised Learning of 3D Semantic Keypoints with Mutual Reconstruction [keypoints]
- Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism [completion]
- Masked Discrimination for Self-Supervised Learning on Point Clouds [self-Supervised; Github]
- FUTR3D: A Unified Sensor Fusion Framework for 3D Detection [det]
- 3DAC: Learning Attribute Compression for Point Clouds [compression]
- CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance [seg]
- DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection [det; Github]
- PointAttN: You Only Need Attention for Point Cloud Completion [completion; PyTorch]
- Deep learning for radar data exploitation of autonomous vehicle [radar, autonomous vehicle]
- LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network [seg]
- Masked Autoencoders for Point Cloud Self-supervised Learning [self-supervised; PyTorch]
- CVFNet: Real-time 3D Object Detection by Learning Cross View Features [det]
- PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows [denoising]
- PETR: Position Embedding Transformation for Multi-View 3D Object Detection [det]
- An Empirical Investigation of 3D Anomaly Detection and Segmentation [anomaly detection; PyTorch]
- A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds [tracking]
- DisARM: Displacement Aware Relation Module for 3D Detection [det]
- Dense Voxel Fusion for 3D Object Detection [det]
- DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association [tracking; Github]
- Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors [anomaly detection]
- PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds [seg]
- Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer [completion; PyTorch]
- LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling [downsampling]
- Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning [self-supervised]
- Edge-Selective Feature Weaving for Point Cloud Matching [correspondence; PyTorch-lightning]
- Neighborhood-aware Geometric Encoding Network for Point Cloud Registration [registration; PyTorch]
- Boosting Monocular Depth Estimation with Sparse Guided Points [monocular, depth estimation; Github]
- Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [autonomous platforms]
- TPC: Transformation-Specific Smoothing for Point Cloud Models [attack]
- ShapeFormer: Transformer-based Shape Completion via Sparse Representation [completion; Github]
- Self-supervised Point Cloud Registration with Deep Versatile Descriptors [registration]
- CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning [self-supervised]
- AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection [det]
- Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision [seg]

2021

ICCV
- FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [monocular, det; mmdet3d]
- Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning [unsupervised]
- Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation [seg]
- Pyramid Point Cloud Transformer for Large-Scale Place Recognition [place recognition; Github]
- Distinctiveness oriented Positional Equilibrium for Point Cloud Registration [registration]
- Feature Interactive Representation for Point Cloud Registration [registration]
- DeepPRO: Deep Partial Point Cloud Registration of Objects [registration]
- LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration [registration; matlab]
- Provably Approximated Point Cloud Registration [registration]
- Point Transformer [seg, cls; PyTorch-unofficial]
- Point Cloud Augmentation with Weighted Local Transformations [augmentation; PyTorch]
- PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds [registration; PyTorch]
- An End-to-End Transformer Model for 3D Object Detection [det; PyTorch]
- Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration [registration; Github]
- Deep Hough Voting for Robust Global Registration [registration; PyTorch]
- Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection [det]
- Voxel Transformer for 3D Object Detection [det]
- Learning Inner-Group Relations on Point Clouds [cls, seg]
- Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds [self-supervised; Github]
- 4D-Net for Learned Multi-Modal Alignment [det]
- AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [monocular, det; Github]
- A Robust Loss for Point Cloud Registration [registration]
- OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration [registration]
- Improving 3D Object Detection with Channel-wise Transformer [det; Github]
- Voxel-based Network for Shape Completion by Leveraging Edge Generation [completion; Github]
- Exploring Simple 3D Multi-Object Tracking for Autonomous Driving [tracking]
- ME-PCN: Point Completion Conditioned on Mask Emptiness [completion]
- Deep Hybrid Self-Prior for Full 3D Mesh Generation [generation]
- Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation [monocular, depth; Github]
- StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation [monocular, depth; PyTorch]
- Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility [reconstruction; Github]
- PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [completion; PyTorch]
- Adaptive Graph Convolution for Point Cloud Analysis [cls, seg; PyTorch]
- RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection [det]
- Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation [monocular, depth; Github]
- Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks [seg; Github]
- Is Pseudo-Lidar needed for Monocular 3D Object detection? [monocular, det; Github]
- Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
- Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis [cls, seg; Github]
- AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds [normal estimation; Github]
- Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather [det; Github]
- Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds [tracking; Github]
- SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [completion; Github]
- DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation [seg]
- RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection [det; Github]
- Hierarchical Aggregation for 3D Instance Segmentation [seg; Github]
- Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation [seg; Github]
- Group-Free 3D Object Detection via Transformers [det; PyTorch]
- VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation [seg; Github]
- Learning with Noisy Labels for Robust Point Cloud Segmentation [seg; Github]
- Geometry Uncertainty Projection Network for Monocular 3D Object Detection [det, monocular]
- ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation [seg; PyTorch]
- Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency [det; OpenPCDet]
- Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion [pre-training; Github]
- HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration [registration; PyTorch]
- Score-Based Point Cloud Denoising [denoising]
- Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows [monocular, pose; Github]
- A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation [seg; Github]
- The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [monocular, det]
CVPR
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation [seg; PyTorch]
- PointAugmenting: Cross-Modal Augmentation for 3D Object Detection [det]
- PVGNet: A Bottom-Up One-Stage 3D Object Detector with Integrated Multi-Level Features [det]
- MetaSets: Meta-Learning on Point Sets for Generalizable Representations [domain]
- LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [seg; PyTorch]
- PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths [completion; PyTorch]
- CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds [correspondence; PyTorch-lightning]
- StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks [registration]
- To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels [det]
- RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection [det]
- Point Cloud Upsampling via Disentangled Refinement [upsampling; Github]
- 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; PyTorch]
- 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]
- Single Image Depth Prediction with Wavelet Decomposition [depth; PyTorch]
- 3D Shape Generation with Grid-based Implicit Functions [generation]
Others
- PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis [cls, seg; PyTorch; TIP]
- DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration [registration; PyTorch; BMVC]
- On Automatic Data Augmentation for 3D Point Cloud Classification [augmentation, cls; BMVC]
- Self-Supervised Point Cloud Completion via Inpainting [completion; BMVC]
- Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation [cls, seg; BMVC]
- 3D Object Tracking with Transformer [tracking; Github; BMVC]
- Cascading Feature Extraction for Fast Point Cloud Registration [registration; BMVC]
- PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars [det, seg; PyTorch; NeurIPS]
- Revisiting 3D Object Detection From an Egocentric Perspective [det; NeurIPS]
- Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion [completion; PyTorch; NeurIPS]
- Multimodal Virtual Point 3D Detection [det; PyTorch; NeurIPS]
- 3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds [tracking; Github; NeurIPS]
- Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image [monocular, det, reconstruction; NeurIPS]
- Accurate Point Cloud Registration with Robust Optimal Transport [registration; Github; NeurIPS]
- CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration [registration; PyTorch; NeurIPS]
- Object DGCNN: 3D Object Detection using Dynamic Graphs [det; Github; NeurIPS]
- Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network [autonomous driving; NeurIPS]
- Probabilistic and Geometric Depth: Detecting Objects in Perspective [det; mmdet3d; CoRL]
- DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries [det; Github; CoRL]
- Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks [autonomous driving; Github; CoRL]
- Semi-supervised 3D Object Detection via Temporal Graph Neural Networks [det]
- GASCN: Graph Attention Shape Completion Network [completion; 3DV]
- DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications [monocular, autonomous driving; Github; 3DV]
- Learning Iterative Robust Transformation Synchronization [transformation synchronization; Github; 3DV]
- DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction [correspondence; PyTorch; 3DV]
- DeepBBS: Deep Best Buddies for Point Cloud Registration [registration; PyTorch; 3DV]
- Similarity-Aware Fusion Network for 3D Semantic Segmentation [seg; Github; IROS]
- Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection [det; ACM MM]
- From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder [det; Github; ACM MM]
- Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud [det; Github; ACM MM]
- Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views [unsupervised; ACM MM]
- SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering [upsampling; Github; ACM MM]
- Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning [self-supervised; ACM MM]
- Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting [monocular, det; ACM MM]
- Fast and Robust Registration of Partially Overlapping Point Clouds [registration; PyTorch; RAL]
- Graph-Guided Deformation for Point Cloud Completion [completion; RAL]
- GIDSeg: Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors [seg; RAL]
- Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration [registration; IJCAI]
- 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]
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [seg, det; PyTorch; TPAMI]
- Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining [seg; TPAMI]
- Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [seg; TPAMI]
- Fast and Robust Iterative Closest Point [registration; Github; TPAMI]
- MonoGRNet: A General Framework for Monocular 3D Object Detection [monocular, det; TPAMI]
- PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences [action recognition, seg; ICLR]
- 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]
- Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline [cls; PyTorch; ICML]
- 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]
- CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection [det; PyTorch; WACV]
arXiv
- ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation [seg, unsupervised domain adaptation; Github]
- COTReg: Coupled Optimal Transport based Point Cloud Registration [registration]
- iSeg3D: An Interactive 3D Shape Segmentation Tool [seg, annotation tool]
- Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results [completion, registration; PyTorch]
- BEVDet: High-Performance Multi-Camera 3D Object Detection in Bird-Eye-View [det; Github]
- Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need? [transformation invariant]
- High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling [completion]
- EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection [det]
- Domain Adaptation on Point Clouds via Geometry-Aware Implicits [domain adaptation]
- Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction [registration; Github]
- Immortal Tracker: Tracklet Never Dies [tracking; Github]
- FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection [det; PyTorch]
- Robust Partial-to-Partial Point Cloud Registration in a Full Range [registration; PyTorch]
- Semi-supervised Implicit Scene Completion from Sparse LiDAR [completion; PyTorch]
- Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [PyTorch]
- Multi-instance Point Cloud Registration by Efficient Correspondence Clustering [registration]
- Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization [registration, non-rigid; Github]
- PU-Transformer: Point Cloud Upsampling Transformer [upsampling]
- GenReg: Deep Generative Method for Fast Point Cloud Registration [registration]
- Deep Point Cloud Reconstruction [reconstruction]
- MFM-Net: Unpaired Shape Completion Network with Multi-stage Feature Matching [completion]
- PointMixer: MLP-Mixer for Point Cloud Understanding [seg, cls, reconstruction]
- Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression [compression; Github]
- What Stops Learning-based 3D Registration from Working in the Real World? [registration]
- CpT: Convolutional Point Transformer for 3D Point Cloud Processing [cls, seg]
- RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation [det; PyTorch]
- IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration [registration]
- SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking [track; Github]
- DRINet++: Efficient Voxel-as-point Point Cloud Segmentation [seg]
- Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion [seg]
- DFC: Deep Feature Consistency for Robust Point Cloud Registration [registration]
- Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
- CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds [seg]
- Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection [det; Github]
- Deep Models with Fusion Strategies for MVP Point Cloud Registration [registration]
- Improved Pillar with Fine-grained Feature for 3D Object Detection [det]
- 3D Object Detection Combining Semantic and Geometric Features from Point Clouds [det]
- How to Build a Curb Dataset with LiDAR Data for Autonomous Driving [autonomous driving]
- 3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation [det, tracking]
- PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds [PyTorch]
- Differentiable Convolution Search for Point Cloud Processing [cls, seg]
- SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering [seg]
- You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors [registration; PyTorch]
- GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network [seg]
- SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation [det]
- Progressive Coordinate Transforms for Monocular 3D Object Detection [monocular, det; Github]
- UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [registration]
- Investigating Attention Mechanism in 3D Point Cloud Object Detection [det; Github]
- Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness [det]
- Probabilistic and Geometric Depth: Detecting Objects in Perspective [det; Github]
- Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [det; Github]
- CarveNet: Carving Point-Block for Complex 3D Shape Completion [completion]
- CKConv: Learning Feature Voxelization for Point Cloud Analysis [cls, seg]
- DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization [det]
- Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters [seg]
- Dynamic Convolution for 3D Point Cloud Instance Segmentation[seg; PyTorch]
- Beyond Farthest Point Sampling in Point-Wise Analysis [sampling]
- Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes [seg]
- Multi-Modality Task Cascade for 3D Object Detection [det; Github]
- Point Cloud Registration using Representative Overlapping Points [registration; PyTorch]
- “Zero Shot” Point Cloud Upsampling [upsampling]
- Shape registration in the time of transformers [registration]
- 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching [registration]
- Image2Point: 3D Point-Cloud Understanding withPretrained 2D ConvNets [cls, seg; Github]
- Z2P: Instant Rendering of Point Clouds [rendering]
- 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]
- 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]
- 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]
- 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]
- 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
- Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots [det]
- 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
CVPR
- End-to-end pseudo-lidar for image-based 3d object detection [det; PyTorch]
- 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]
- PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with Point-Based Attentive Cont-Conv Fusion Module [det; AAAI]
- 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]
- SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud [det; ICRA]
- 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]
- 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(Nico) [cls, seg]
- Deterministic PointNetLK for Generalized Registration [registration]
- 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
- Disentangling Monocular 3D Object Detection [det, monocular]
- 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
- Multi-Task Multi-Sensor Fusion for 3D Object Detection [det]
- 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
- End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds [det; CoRL]
- 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]
- Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection [det; PyTorch; IROS]
- RangeNet++: Fast and Accurate LiDAR Semantic Segmentation [seg; PyTorch; IROS]
- IoU Loss for 2D/3D Object Detection [det; 3DV]
- 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
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds [det; PyTorch; ECCVW]
- 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]
- Classification of Point Cloud Scenes with Multiscale Voxel Deep Network [seg]

2017

CVPR
- Fine-To-Coarse Global Registration of RGB-D Scans [registration; Github]
- 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]
2013
- Vision meets robotics: The kitti dataset [IJRR]
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/XuyangBai/awesome-point-cloud-registration
https://github.com/weiweisun2018/awesome-point-clouds-registration
https://github.com/chaytonmin/Awesome-BEV-Perception-Multi-Cameras

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
pointcloudset: https://github.com/virtual-vehicle/pointcloudset

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

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README.md

README.md

3D - Point Cloud

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Before 2016

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3D - Point Cloud

Survey papers

2022

2021

2020

2019

2018

2017

Before 2016

Resources

Tools

Software