Paper list and Datasets about Point Cloud. Datasets can be found in Datasets.md.
- Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey [arXiv 2023]
- Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe [arXiv 2022; Github]
- 3D Vision with Transformers: A Survey [arXiv 2022; Github]
- Vision-Centric BEV Perception: A Survey [arXiv 2022; Github]
- 3D Object Detection for Autonomous Driving: A Review and New Outlooks [arXiv 2022; Github]
- 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 Point Cloud Representation Learning with Deep Neural Networks: A Survey [arXiv 2022; Github]
- Multi-modal Sensor Fusion for Auto Driving Perception: A Survey [arXiv 2022]
- 3D Object Detection from Images for Autonomous Driving: A Survey [arXiv 2022; Github]
- Survey and Systematization of 3D Object Detection Models and Methods [arXiv 2022]
- 3D Object Detection for Autonomous Driving: A Survey [Pattern Recognition 2022; Github]
- 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; Github]
- A Comprehensive Performance Evaluation of 3D Local Feature Descriptors [IJCV 2016]
- CVPR
- MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving [
seg; PyTorch] - MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences [
det; Github] - Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency [
det; Github] - Rotation-Invariant Transformer for Point Cloud Matching [
matching] - Frequency-Modulated Point Cloud Rendering with Easy Editing [
rendering; PyTorch] - Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis [
cls,seg,det; PyTorch] - PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection [
det; PyTorch] - SCPNet: Semantic Scene Completion on Point Cloud [
semantic scene completion] - Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection [
det; Github] - Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection [
det; Github] - LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion [
det; Github] - X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection [
det] - Virtual Sparse Convolution for Multimodal 3D Object Detection [
det; PyTorch] - Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly [
reconstruction] - ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion [
completion; Github] - PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees [
cls] - Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View [
det] - Neural Intrinsic Embedding for Non-rigid Point Cloud Matching [
non-rigid matching] - SBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution [
seg] - Multimodal Industrial Anomaly Detection via Hybrid Fusion [
anomaly detection; Github] - ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer [
completion; PyTorch] - Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting [
autonomous driving; PyTorch] - VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion [
autonomous driving; Github] - Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction [
autonomous driving; PyTorch] - CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP [
pre-training] - ConQueR: Query Contrast Voxel-DETR for 3D Object Detection [
det; Github] - Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders [
pre-training; Github] - GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds [
pre-training; Github] - BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision [
autonomous driving] - MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection [
det; Github] - DETRs with Hybrid Matching [
det; Github] - LaserMix for Semi-Supervised LiDAR Semantic Segmentation [
seg; PyTorch] - PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection [
det; Github]
- MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving [
- AAAI
- Parametric Surface Constrained Upsampler Network for Point Cloud [
upsampling; PyTorch] - CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame [
rotation invariance] - PUPS: Point Cloud Unified Panoptic Segmentation [
seg] - StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection [
det] - Rethinking Rotation Invariance with Point Cloud Registration [
cls,seg,retrieval; PyTorch] - SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud [
det; Github] - CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection [
det; Github] - MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection [
det; PyTorch] - Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection [
det; Github] - NeAF: Learning Neural Angle Fields for Point Normal Estimation [
normal estimation; PyTorch] - Superpoint Transformer for 3D Scene Instance Segmentation [
seg; PyTorch] - Language-Assisted 3D Feature Learning for Semantic Scene Understanding [
det,seg; Github] - CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion [
semantic scene completion; Github] - Transformation-Equivariant 3D Object Detection for Autonomous Driving [
det] - PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples [
completion] - GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds [
tracking; Github] - Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics [
normal estimation] - A Simple Baseline for Multi-Camera 3D Object Detection [
det; Github] - PolarFormer: Multi-camera 3D Object Detection with Polar Transformer [
det; PyTorch] - BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection [
det; PyTorch] - BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo [
det; PyTorch]
- Parametric Surface Constrained Upsampler Network for Point Cloud [
- Others
- RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations [
registration; PyTorch; TPAMI] - CircNet: Meshing 3D Point Clouds with Circumcenter Detection [
triangulation; Github; ICLR] - BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection [
det; Github; ICLR] - Bidirectional Propagation for Cross-Modal 3D Object Detection [
det; PyTorch; ICLR] - Exploring Active 3D Object Detection from a Generalization Perspective [
det; PyTorch; ICLR] - DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection [
det; Github; ICRA] - Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion [
completion; ICRA] - A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation [
registration; ICRA] - LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR [
completion; PyTorch; ICRA] - DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving [
det; ICRA] - Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention [
seg; ICRA] - MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion [
det; ICRA] - MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts [
det; ICRA] - SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping [
completion; PyTorch; ICRA] - BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [
autonomous driving; PyTorch; ICRA] - CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection [
det; Github; ICRA] - ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation [
seg,unsupervised domain adaptation; Github; ICRA] - MonoEdge: Monocular 3D Object Detection Using Local Perspectives [
det; WACV] - Far3Det: Towards Far-Field 3D Detection [
det; WACV] - Centroid Distance Keypoint Detector for Colored Point Clouds [
keypoint; Github; WACV] - NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds [
seg; Project; WACV]
- RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations [
- arXiv
- SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving [
autonomous driving; PyTorch] - DiffBEV: Conditional Diffusion Model for Bird's Eye View Perception [
seg,det; PyTorch] - GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue [
seg] - BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection [
det; PyTorch] - Modeling Continuous Motion for 3D Point Cloud Object Tracking [
tracking] - Category-Level Multi-Part Multi-Joint 3D Shape Assembly [
shape assembly] - Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception [
autonomous driving] - Rethinking Range View Representation for LiDAR Segmentation [
seg] - Efficient Transformer-based 3D Object Detection with Dynamic Token Halting [
det] - ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data [
seg; Github] - InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data [
seg; Github] - Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training [
pre-training] - OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion [
autonomous driving; PyTorch] - DA-BEV: Depth Aware BEV Transformer for 3D Object Detection [
det] - Pillar R-CNN for Point Cloud 3D Object Detection [
det] - 2D-Empowered 3D Object Detection on the Edge [
det] - General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment [
cls,seg,det] - PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis [
cls,seg] - Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking [
tracking; Github] - Top-Down Beats Bottom-Up in 3D Instance Segmentation [
seg; PyTorch] - TR3D: Towards Real-Time Indoor 3D Object Detection [
det; PyTorch] - CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data [
place recognition; Github] - AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation [
det,seg] - Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline [
det; PyTorch] - On the Adversarial Robustness of Camera-based 3D Object Detection [
det] - RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving [
seg; Github] - FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer [
det] - Joint Representation Learning for Text and 3D Point Cloud [
pre-training; Github] - Long Range Pooling for 3D Large-Scale Scene Understanding [
seg] - OA-BEV: Bringing Object Awareness to Bird’s-Eye-View Representation for Multi-Camera 3D Object Detection [
det] - SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation [
seg] - FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection [
det] - AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers [
completion] - Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection [
lane det] - Object as Query: Equipping Any 2D Object Detector with 3D Detection Ability [
det] - Super Sparse 3D Object Detection [
det; PyTorch] - Model-Agnostic Hierarchical Attention for 3D Object Detection [
det; Github] - Ponder: Point Cloud Pre-training via Neural Rendering [
self-supervised learning; Github] - Cross Modal Transformer via Coordinates Encoding for 3D Object Dectection [
det; Github] - Benchmarking the Robustness of LiDAR Semantic Segmentation Models [
seg]
- SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving [
- ECCV
- PCR-CG: Point Cloud Registration via Color and Geometry [
registration; PyTorch] - SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement [
cls,seg; PyTorch] - Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection [
det; PyTorch] - LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation [
seg; Github] - Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection [
det] - Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining [
det] - CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection [
det] - LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [
seg] - Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [
det; PyTorch] - SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds [
det] - LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds [
NAS] - FBNet: Feedback Network for Point Cloud Completion [
completion; PyTorch] - INT: Towards Infinite-frames 3D Detection with An Efficient Framework [
det] - MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection [
det; Github] - PointInst3D: Segmenting 3D Instances by Points [
seg; Github] - PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection [
det; PyTorch] - GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation [
autonomous driving] - Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter [
seg; Github] - Multimodal Transformer for Automatic 3D Annotation and Object Detection [
det; PyTorch] - Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes [
seg; PyTorch] - Online Segmentation of LiDAR Sequences: Dataset and Algorithm [
seg; PyTorch] - SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse views [
reconstruction; PyTorch] - Language-Grounded Indoor 3D Semantic Segmentation in the Wild [
seg; PyTorch] - CenterFormer: Center-based Transformer for 3D Object Detection [
det; Github] - Learning to Generate Realistic LiDAR Point Clouds [
generation; Project] - PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration [
registration; Github] - SimpleRecon: 3D Reconstruction Without 3D Convolutions [
reconstruction; Project] - Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation [
registration; Github] - Masked Discrimination for Self-Supervised Learning on Point Clouds [
self-supervised; PyTorch] - MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes [
dense captioning; Github] - Objects Can Move: 3D Change Detection by Geometric Transformation Constistency [
change detection; Github] - PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees [
cls,seg; PyTorch] - diffConv: Analyzing Irregular Point Clouds with an Irregular View [
cls,seg; PyTorch] - SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty [
de-snowing] - Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph [
det; PyTorch] - Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding [
4D] - Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network [
reconstruction] - PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking? [
tracking] - MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud [
representation] - Large-displacement 3D Object Tracking with Hybrid Non-local Optimization [
tracking; Github] - ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection [
det; Github] - Semi-supervised 3D Object Detection with Proficient Teachers [
det; Github] - Monocular 3D Object Detection with Depth from Motion [
det; PyTorch] - GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation [
normal estimation; PyTorch] - PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation [
completion; PyTorch] - Label-Guided Auxiliary Training Improves 3D Object Detector [
det; Github] - Salient Object Detection for Point Clouds [
det; Code] - 3D Siamese Transformer Network for Single Object Tracking on Point Clouds [
tracking; Github] - Point Cloud Compression with Sibling Context and Surface Priors [
compression; PyTorch] - SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000× Fewer Labels [
seg; Tensorflow] - PointMixer: MLP-Mixer for Point Cloud Understanding [
seg,cls,reconstruction; PyTorch] - DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection [
det,monocular; PyTorch] - Dynamic 3D Scene Analysis by Point Cloud Accumulation [
accumulation; PyTorch] - MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis [
self-supervised] - SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer [
completion; PyTorch] - Unsupervised Deep Multi-Shape Matching [
matching] - Monocular 3D Object Reconstruction with GAN Inversion [
reconstruction; PyTorch] - CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation [
seg; PyTorch] - GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation [
seg; PyTorch] - Densely Constrained Depth Estimator for Monocular 3D Object Detection [
det,monocular; PyTorch] - SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud [
registration; Tensorflow] - What Matters for 3D Scene Flow Network [
scene flow; PyTorch] - Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [
seg] - Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes [
reconstruction; Github] - JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes [
autonomous driving; PyTorch] - TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance [
pose estimation; PyTorch] - CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement [
pose estimation; PyTorch] - Lidar Point Cloud Guided Monocular 3D Object Detection [
det,monocular; PyTorch] - DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection [
det,monocular; PyTorch] - ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [
autonomous driving; PyTorch] - 3D Instances as 1D Kernels [
seg; PyTorch] - Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation [
scene flow; PyTorch] - Rethinking IoU-based Optimization for Single-stage 3D Object Detection [
det; Github] - Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds [
sampling] - CPO: Change Robust Panorama to Point Cloud Localization [
visual localization] - Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks [
pose estimation; PyTorch] - Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting [
autonomous driving; PyTorch] - 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds [
seg; Github] - A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision [
self-supervised] - Open-world Semantic Segmentation for LIDAR Point Clouds [
seg; PyTorch] - BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers [
det,seg; Github] - LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection [
det; PyTorch] - FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection [
det; PyTorch] - Masked Autoencoders for Point Cloud Self-supervised Learning [
self-supervised; PyTorch] - PETR: Position Embedding Transformation for Multi-View 3D Object Detection [
det; PyTorch] - Learning Ego 3D Representation as Ray Tracing [
autonomous driving; PyTorch] - AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection [
det; Github]
- PCR-CG: Point Cloud Registration via Color and Geometry [
- CVPR
- Bridged Transformer for Vision and Point Cloud 3D Object Detection [
det] - ShapeFormer: Transformer-based Shape Completion via Sparse Representation [
completion; PyTorch] - RBGNet: Ray-based Grouping for 3D Object Detection [
det; Github] - Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [
det] - 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]
- Bridged Transformer for Vision and Point Cloud 3D Object Detection [
- AAAI
- SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving [
det,tracking; PyTorch] - Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection [
det; PyTorch] - Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders [
seg] - SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [
det; PyTorch] - DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature Extraction [
cls,seg; PyTorch] - Reliable Inlier Evaluation for Unsupervised Point Cloud Registration [
registration; Github] - Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation [
seg] - FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration [
registration; Github] - DetarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration [
registration; Github] - AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds [
det] - Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds [
det,tracking] - Attention-based Transformation from Latent Features to Point Clouds [
generation] - Behind the Curtain: Learning Occluded Shapes for 3D Object Detection [
det; PyTorch]
- SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving [
- Others
- Fast-BEV: Towards Real-time On-vehicle Bird’s-Eye View Perception [
det; Github; NeurIPSW] - Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection [
det; Github; NeurIPS] - Language Conditioned Spatial Relation Reasoning for 3D Object Grounding [
localizing; PyTorch; NeurIPS] - A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation [
seg; NeurIPSW] - Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception [
autonomous driving; NeurIPSW] - HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces [
normal estimation; Github; NeurIPS] - LION: Latent Point Diffusion Models for 3D Shape Generation [
generation; Github; NeurIPS] - SageMix: Saliency-Guided Mixup for Point Clouds [
augmentation; Github; NeurIPS] - Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection [
det; Github; NeurIPS] - Point Transformer V2: Grouped Vector Attention and Partition-based Pooling [
cls,seg; Github; NeurIPS] - Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis [
cross-modality; Github; NeurIPS] - OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds [
seg; PyTorch; NeurIPS] - CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [
det; Github; NeurIPS] - Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds [
reconstruction; PyTorch; NeurIPS] - PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds [
augmentation; PyTorch; NeurIPS] - Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [
det; NeurIPS] - P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting [
pre-training; PyTorch; NeurIPS] - Fully Sparse 3D Object Detection [
det; PyTorch; NeurIPS] - PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies [
seg,cls; PyTorch; NeurIPS] - K-Radar: 4D Radar Object Detection Dataset and Benchmark for Autonomous Driving in Various Weather Conditions [
autonomous driving; Github; NeurIPS Track] - SNAKE: Shape-aware Neural 3D Keypoint Field [
keypoints; PyTorch; NeurIPS] - Unifying Voxel-based Representation with Transformer for 3D Object Detection [
det; PyTorch; NeurIPS] - Non-rigid Point Cloud Registration with Neural Deformation Pyramid [
non-rigid,registration; PyTorch; NeurIPS] - DeepInteraction: 3D Object Detection via Modality Interaction [
det; Github; NeurIPS] - Spatial Pruned Sparse Convolution for Efficient 3D Object Detection [
det; NeurIPS] - Rethinking the compositionality of point clouds through regularization in the hyperbolic space [
cls; Github; NeurIPS] - Cross-modal Learning for Image-Guided Point Cloud Shape Completion [
completion; Github; NeurIPS] - Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset [
pose estimation; PyTorch; NeurIPS] - GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions [
representation; PyTorch; NeurIPS] - Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training [
self-supervised; PyTorch; NeurIPS] - Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation [
seg; PyTorch; ACCV] - Point Cloud Upsampling via Cascaded Refinement Network [
upsampling; Github; ACCV] - Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection [
det; PyTorch; BMVC] - APSNet: Attention Based Point Cloud Sampling [
sampling; PyTorch; BMVC] - Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [
unsupervised; PyTorch; BMVC] - COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation [
seg; PyTorch; BMVC] - GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing [
registration; Github; TIP] - 3D Cascade RCNN: High Quality Object Detection in Point Clouds [
det; PyTorch; TIP] - Real-time 3D Single Object Tracking with Transformer [
tracking; PyTorch; TMM] - Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds [
seg; Github; 3DV] - SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence [
non-rigid,matching; Github; 3DV] - SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation [
cls,seg; Github; 3DV] - Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes [
seg; 3DV] - Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians [
upsampling; 3DV] - CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion [
tracking; Project; CoRL] - Towards Long-Tailed 3D Detection [
det; CoRL] - CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers [
seg; PyTorch; CoRL] - SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving [
motion forecasting; PyTorch; CoRL] - MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation [
seg; ICME] - CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving [
seg; PyTorch; ICME] - Learning to Generate 3D Shapes from a Single Example [
generation; PyTorch; SIGGRAPH Aisa] - Shape Completion with Points in the Shadow [
completion; SIGGRAPH Aisa] - ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs [
registration; SIGGRAPH] - Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection [
det; ACM MM] - GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression [
compressing; Github; ACM MMW] - Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network [
det; Github; ACM MM] - You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors [
registration; PyTorch; ACM MM] - Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds [
det; ACM MM] - LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud [
autonomous driving; IROS] - Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud [
seg; Github; IROS] - Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation [
seg; PyTorch; IROS] - 3D Part Assembly Generation with Instance Encoded Transformer [
part assembly; IROS] - BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR [
place recognition; IROS] - Benchmarking and Analyzing Point Cloud Classification under Corruptions [
cls; PyTorch; ICML] - AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection [
det; IJCAI] - 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] - Robust Point Cloud Segmentation with Noisy Annotations [
seg; PyTorch; TPAMI] - Robust Point Cloud Registration Framework Based on Deep Graph Matching [
registration; PyTorch; TPAMI] - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [
autonomous driving; PyTorch; TPAMI] - RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration [
registration; TPAMI] - Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization [
det; TPAMI] - Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds [
det; TPAMI] - Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes [
det; TPAMI] - 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] - A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration [
registration; Github; TPAMI] - DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors [
det; PyTorch; TPAMI] - Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer [
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] - Robust 3D Object Detection in Cold Weather Conditions [
det; IV] - urban_road_filter: A real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles [
det,autonomous driving; Github; Video; Sensors] - GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration [
registration; RAL] - Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking [
tracking; Github; RAL] - DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association [
tracking; Github; RAL] - BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling [
upsampling; RAL] - Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions [
seg; Github; RAL] - Temporal Point Cloud Completion with Pose Disturbance [
completion; RAL] - Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation [
seg; Tensorflow; CVM] - 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]
- Fast-BEV: Towards Real-time On-vehicle Bird’s-Eye View Perception [
- arXiv
- TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning [
det; Github] - MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency [
seg,det] - ADAS: A Simple Active-and-Adaptive Baseline for Cross-Domain 3D Semantic Segmentation [
seg; PyTorch] - OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection [
det; Github] - 3D Point Cloud Pre-training with Knowledge Distillation from 2D Images [
pre-training] - Point-E: A System for Generating 3D Point Clouds from Complex Prompts [
generation; PyTorch] - DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention [
det] - MAELi -- Masked Autoencoder for Large-Scale LiDAR Point Clouds [
self-supervised] - ULIP: Learning Unified Representation of Language, Image and Point Cloud for 3D Understanding [
cls; Github] - BEV-MAE: Bird's Eye View Masked Autoencoders for Outdoor Point Cloud Pre-training [
pre-training] - Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds [
seg] - SemanticBEVFusion: Rethink LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection [
det] - SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation [
completion,reconstruction,generation; Github] - Fast Point Cloud Generation with Straight Flows [
generation] - PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models [
seg] - GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation [
reconstruction; PyTorch] - Language-driven Open-Vocabulary 3D Scene Understanding [
open-vocabulary; Github] - 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers [
det] - Meta Architecure for Point Cloud Analysis [
seg,cls; PyTorch] - Learning 3D Scene Priors with 2D Supervision [
layout,shape; Project] - PVT3D: Point Voxel Transformers for Place Recognition from Sparse Lidar Scans [
place recognition] - ONeRF: Unsupervised 3D Object Segmentation from Multiple Views [
seg,reconstruction] - AeDet: Azimuth-invariant Multi-view 3D Object Detection [
det] - Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion [
det] - MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception [
seg,det] - PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning [
pre-training; Github] - Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis [
cls,seg] - 3D-QueryIS: A Query-based Framework for 3D Instance Segmentation [
seg] - You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning [
det] - Towards 3D Object Detection with 2D Supervision [
det] - Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection [
det] - Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning [
self-supervised; Github] - 3D Reconstruction of Multiple Objects by mmWave Radar on UAV [
reconstruction] - Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection [
det] - Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud Classification [
cls; PyTorch] - LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations [
augmentation] - Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training [
det] - Zero-shot Point Cloud Segmentation by Transferring Geometric Primitives [
seg] - Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement [
det] - Mask3D for 3D Semantic Instance Segmentation [
seg] - STD: Stable Triangle Descriptor for 3D place recognition [
place recognition; Github] - Neural Wavelet-domain Diffusion for 3D Shape Generation [
generation] - FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection [
det; Github] - LidarMultiNet: Towards a Unified Multi-task Network for LiDAR Perception [
det] - 4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds [
autonomous driving] - Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking [
tracking] - CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer [
det] - M^2-3DLaneNet: Multi-Modal 3D Lane Detection [
det] - Multi-modal Streaming 3D Object Detection [
det] - ISS: Image as Stetting Stone for Text-Guided 3D Shape Generation [
generation] - CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion [
tracking] - SEFormer: Structure Embedding Transformer for 3D Object Detection [
det] - Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions [
robustness; PyTorch] - Scatter Points in Space: 3D Detection from Multi-view Monocular Images [
det] - Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image [
reconstruction; PyTorch] - PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection [
det] - Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction [
reconstruction] - Pix4Point: Image Pretrained Transformers for 3D Point Cloud Understanding [
pretraining; Github] - Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection [
det] - AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [
det] - PeRFception: Perception using Radiance Fields [
NeRF; PyTorch] - Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking [
tracking] - STS: Surround-view Temporal Stereo for Multi-view 3D Detection [
det] - PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition [
adversarial attack] - PersDet: Monocular 3D Detection in Perspective Bird’s-Eye-View [
det] - InterTrack: Interaction Transformer for 3D Multi-Object Tracking [
tracking] - An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection [
det] - RWSeg: Cross-graph Competing Random Walks for Weakly Supervised 3D Instance Segmentation [
seg] - Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer [
tracking; PyTorch] - RadSegNet: A Reliable Approach to Radar Camera Fusion [
autonomous driving] - Aerial Monocular 3D Object Detection [
det; Project] - TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection [
det] - Point-McBert: A Multi-choice Self-supervised Framework for Point Cloud Pre-training [
self-supervised] - MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones [
det; PyTorch] - DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection [
det] - On the Robustness of 3D Object Detectors [
det] - Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild [
det; PyTorch] - Boosting 3D Object Detection via Object-Focused Image Fusion [
det; PyTorch] - Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness [
det; Github] - Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection [
det,monocular] - UniFormer: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View [
autonomous driving] - PointNorm: Normalization is All You Need for Point Cloud Analysis [
cls,seg; Github] - SpOT: Spatiotemporal Modeling for 3D Object Tracking [
tracking] - CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm [
completion] - Implicit Autoencoder for Point Cloud Self-supervised Representation Learning [
self-supervised; PyTorch] - Learning to Register Unbalanced Point Pairs [
registration] - Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation [
seg] - MT-Net Submission to the Waymo 3D Detection Leaderboard [
det] - Masked Surfel Prediction for Self-Supervised Point Cloud Learning [
self-supervised; Github] - GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [
det] - Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning [
det] - ORA3D: Overlap Region Aware Multi-view 3D Object Detection [
det] - Masked Autoencoders in 3D Point Cloud Representation Learning [
self-supervised] - Masked Autoencoders for Self-Supervised Learning on Automotive Point Clouds [
self-supervised] - SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds [
registration; PyTorch] - HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction [
reconstruction; Github] - NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors [
reconstruction; Project] - Unseen Object 6D Pose Estimation: A Benchmark and Baselines [
pose estimation; Project] - LidarMutliNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network [
seg,det] - Polar Parametrization for Vision-based Surround-View 3D Detection [
det; Github] - Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior [
autonomous driving] - Voxel-MAE: Masked Autoencoders for Pre-training Large-scale Point Clouds [
det; Github] - VectorMapNet: End-to-end Vectorized HD Map Learning [
autonomous driving; Github] - A Simple Baseline for BEV Perception Without LiDAR [
autonomous driving; PyTorch] - Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot [
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] - PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories [
completion; Project] - AGConv: Adaptive Graph Convolution on 3D Point Clouds [
cls,seg; PyTorch] - VN-Transformer: Rotation-Equivariant Attention for Vector Neurons [
cls,motion forecasting] - 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] - PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images [
autonomous driving] - SparseDet: Towards End-to-End 3D Object Detection [
det] - 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] - 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] - 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] - Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training [
det,monocular] - 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] - M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation [
det,seg; Project] - 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] - 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] - 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] - FUTR3D: A Unified Sensor Fusion Framework for 3D Detection [
det; PyTorch] - 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] - CVFNet: Real-time 3D Object Detection by Learning Cross View Features [
det] - PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows [
denoising] - 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] - 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] - 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] - Self-supervised Point Cloud Registration with Deep Versatile Descriptors [
registration] - CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning [
self-supervised] - Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision [
seg]
- TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning [
- ICCV
- erception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation [
seg] - MVTN: Multi-View Transformation Network for 3D Shape Recognition [
cls,retrieval; PyTorch] - Self-Supervised Pretraining of 3D Features on any Point-Cloud [
self-supervised; PyTorch] - MGNet: Monocular Geometric Scene Understanding for Autonomous Driving [
autonomous driving; PyTorch] - 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]
- erception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation [
- CVPR
- Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [
self-supervised; PyTorch] - Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models [
self-supervised] - 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 [
registration; 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]
- Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [
- 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]
- PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis [
- arXiv
- 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] - Robust Partial-to-Partial Point Cloud Registration in a Full Range [
registration; PyTorch] - Semi-supervised Implicit Scene Completion from Sparse LiDAR [
completion; 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] - 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] - 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] - 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] - 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] - 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
- Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition [
place recognition; Tensorflow]
- COTReg: Coupled Optimal Transport based Point Cloud Registration [
- 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
- Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots [
- 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]
- End-to-end pseudo-lidar for image-based 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]
- 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]
- 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] - 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]
- Multi-Modality Cut and Paste for 3D Object Detection [
- ICCV
- M3D-RPN: Monocular 3D Region Proposal Network for Object Detection [
det] - 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]
- M3D-RPN: Monocular 3D Region Proposal Network for Object Detection [
- CVPR
- Deep Fitting Degree Scoring Network for Monocular 3D Object Detection [
det,monocular] - 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]
- Deep Fitting Degree Scoring Network for Monocular 3D Object Detection [
- 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]
- End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds [
- 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
- PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation [
det] - 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]
- PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation [
- 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]
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds [
- 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] - Classification of Point Cloud Scenes with Multiscale Voxel Deep Network [
seg]
- Spherical Convolutional Neural Network
for 3D Point Clouds [
- 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]
- Fine-To-Coarse Global Registration of RGB-D Scans [
- 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
- 2013
- 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/XuyangBai/awesome-point-cloud-registration
- https://github.com/weiweisun2018/awesome-point-clouds-registration
- https://github.com/chaytonmin/Awesome-BEV-Perception-Multi-Cameras
- https://github.com/dragonlong/Trending-in-3D-Vision
- https://github.com/4DVLab/Vision-Centric-BEV-Perception
- https://github.com/autodriving-heart/Awesome-occupancy-perception
- 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