awesome-point-cloud-analysis

[ ] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [ ][ ] [ ]

[ ] Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. [ ]

[ ] SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation. [ ] [ ]

[ ] ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. [ ][ ] [ ]

[ ] Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity. [ ]

[ ] Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures. [ ] [ ]

[ ] Discriminative Optimization: Theory and Applications to Point Cloud Registration. [ ]

[ ] 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder. [ ] [ ]

[ ] Multi-View 3D Object Detection Network for Autonomous Driving. [ ] [ ]

[ ] 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions. [ ] [ ]

[ ] OctNet: Learning Deep 3D Representations at High Resolutions. [ ] [ ]

[ ] Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. [ ] [ ]

[ ] 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds. [ ] [ ]

[ ] Colored Point Cloud Registration Revisited. [ ]

[ ] PolyFit: Polygonal Surface Reconstruction from Point Clouds. [ ] [ ]

[ ] From Point Clouds to Mesh using Regression. [ ]

[ ] 3D Graph Neural Networks for RGBD Semantic Segmentation. [ ] [ ]

[ ] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. [ ][ ] [ ]

[ ] Deep Sets. [ ] [ ]

[ ] Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. [ ] [ ]

[ ] Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. [ ] [ ]

[ ] SegMatch: Segment based place recognition in 3D point clouds. [ ]

[ ] Using 2 point+normal sets for fast registration of point clouds with small overlap. [ ]

[ ] Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework. [ ]

[ ] 3D object classification with point convolution network. [ ]

[ ] 3D fully convolutional network for vehicle detection in point cloud. [ ] [ ]

[ ] Deep learning of directional truncated signed distance function for robust 3D object recognition. [ ]

[ ] Analyzing the quality of matched 3D point clouds of objects. [ ]

[ ] SEGCloud: Semantic Segmentation of 3D Point Clouds. [ ] [ ]

[ ] Structure-aware Data Consolidation. [ ]

[ ] SPLATNet: Sparse Lattice Networks for Point Cloud Processing. [ ] [ ]

[ ] Attentional ShapeContextNet for Point Cloud Recognition. [ ]

[ ] Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling. [ ] [ ]

[ ] FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation. [ ] [ ]

[ ] Pointwise Convolutional Neural Networks. [ ] [ ]

[ ] PU-Net: Point Cloud Upsampling Network. [ ] [ ]

[ ] SO-Net: Self-Organizing Network for Point Cloud Analysis. [ ] [ ]

[ ] Recurrent Slice Networks for 3D Segmentation of Point Clouds. [ ] [ ]

[ ] 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. [ ] [ ]

[ ] Deep Parametric Continuous Convolutional Neural Networks. [ ]

[ ] PIXOR: Real-time 3D Object Detection from Point Clouds. [ ] [ ]

[ ] SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation. [ ] [ ]

[ ] Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. [ ] [ ]

[ ] VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. [ ] [ ]

[ ] Reflection Removal for Large-Scale 3D Point Clouds. [ ]

[ ] Hand PointNet: 3D Hand Pose Estimation using Point Sets. [ ] [ ]

[ ] PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition. [ ] [ ]

[ ] A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation. [ ]

[ ] Density Adaptive Point Set Registration. [ ] [ ]

[ ] A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds. [ ]

[ ] Inverse Composition Discriminative Optimization for Point Cloud Registration. [ ]

[ ] CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles. [ ]

[ ] PPFNet: Global Context Aware Local Features for Robust 3D Point Matching. [ ]

[ ] PointGrid: A Deep Network for 3D Shape Understanding. [ ] [ ]

[ ] PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation. [ ] [ ]

[ ] Frustum PointNets for 3D Object Detection from RGB-D Data. [ ] [ ]

[ ] Tangent Convolutions for Dense Prediction in 3D. [ ] [ ]

[ ] Multiresolution Tree Networks for 3D Point Cloud Processing. [ ] [ ]

[ ] EC-Net: an Edge-aware Point set Consolidation Network. [ ] [ ]

[ ] 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation. [ ]

[ ] Learning and Matching Multi-View Descriptors for Registration of Point Clouds. [ ]

[ ] 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. [ ] [ ]

[ ] Local Spectral Graph Convolution for Point Set Feature Learning. [ ] [ ]

[ ] SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. [ ] [ ]

[ ] Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search. [ ]

[ ] Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields. [ ]

[ ] Fully-Convolutional Point Networks for Large-Scale Point Clouds. [ ] [ ]

[ ] Deep Continuous Fusion for Multi-Sensor 3D Object Detection. [ ]

[ ] HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration. [ ]

[ ] Point-to-Point Regression PointNet for 3D Hand Pose Estimation. [ ]

[ ] PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. [ ]

[ ] 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues. [ ]

[ ] YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud. [ ]

[ ] Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction. [ ] [ ]

[ ] Adaptive Graph Convolutional Neural Networks. [ ]

[ ] Unsupervised Learning of Shape and Pose with Differentiable Point Clouds. [ ] [ ]

[ ] PointCNN: Convolution On X-Transformed Points. [ ][ ] [ ]

[ ] Learning Representations and Generative Models for 3D Point Clouds. [ ] [ ]

[ ] Point Convolutional Neural Networks by Extension Operators. [ ] [ ]

[ ] P2P-NET: Bidirectional Point Displacement Net for Shape Transform. [ ] [ ]

[ ] Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. [ ] [ ]

[ ] Learning local shape descriptors from part correspondences with multi-view convolutional networks. [ ] [ ]

[ ] PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition. [ ]

[ ] RGCNN: Regularized Graph CNN for Point Cloud Segmentation. [ ] [ ]

[ ] Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction. [ ]

[ ] End-to-end Learning of Multi-sensor 3D Tracking by Detection. [ ]

[ ] Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping. [ ]

[ ] SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. [ ] [ ]

[ ] Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications. [ ]

[ ] High-Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [ ]

[ ] Sampled-Point Network for Classification of Deformed Building Element Point Clouds. [ ]

[ ] Gemsketch: Interactive Image-Guided Geometry Extraction from Point Clouds. [ ]

[ ] Signature of Topologically Persistent Points for 3D Point Cloud Description. [ ]

[ ] A General Pipeline for 3D Detection of Vehicles. [ ]

[ ] Robust and Fast 3D Scan Alignment Using Mutual Information. [ ]

[ ] Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities. [ ]

[ ] Surface-Based Exploration for Autonomous 3D Modeling. [ ]

[ ] Deep Lidar CNN to Understand the Dynamics of Moving Vehicles. [ ]

[ ] Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [ ]

[ ] Real-Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [ ]

[ ] Robust Generalized Point Cloud Registration Using Hybrid Mixture Model. [ ]

[ ] A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration. [ ]

[ ] Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping. [ ]

[ ] Direct Visual SLAM Using Sparse Depth for Camera-LiDAR System. [ ]

[ ] Spatiotemporal Learning of Dynamic Gestures from 3D Point Cloud Data. [ ]

[ ] Asynchronous Multi-Sensor Fusion for 3D Mapping and Localization. [ ]

[ ] Complex Urban LiDAR Data Set. [ ] [ ]

[ ] CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks.[ ] [ ]

[ ] Dynamic Scaling Factors of Covariances for Accurate 3D Normal Distributions Transform Registration. [ ]

[ ] A 3D Laparoscopic Imaging System Based on Stereo-Photogrammetry with Random Patterns. [ ]

[ ] Robust Generalized Point Cloud Registration with Expectation Maximization Considering Anisotropic Positional Uncertainties. [ ]

[ ] Octree map based on sparse point cloud and heuristic probability distribution for labeled images. [ ]

[ ] PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization. [ ]

[ ] Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map. [ ]

[ ] LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[ ] [ ]

[ ] Classification of Hanging Garments Using Learned Features Extracted from 3D Point Clouds. [ ]

[ ] Stereo Camera Localization in 3D LiDAR Maps. [ ]

[ ] Joint 3D Proposal Generation and Object Detection from View Aggregation. [ ]

[ ] Joint Point Cloud and Image Based Localization for Efficient Inspection in Mixed Reality. [ ]

[ ] Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding. [ ]

[ ] NDVI Point Cloud Generator Tool Using Low-Cost RGB-D Sensor. [ ][ ]

[ ] A 3D Convolutional Neural Network Towards Real-Time Amodal 3D Object Detection. [ ]

[ ] Extracting Phenotypic Characteristics of Corn Crops Through the Use of Reconstructed 3D Models. [ ]

[ ] PCAOT: A Manhattan Point Cloud Registration Method Towards Large Rotation and Small Overlap. [ ]

[ ] [ ]3DmFV: Point Cloud Classification and segmentation for unstructured 3D point clouds. [ ]

[ ] Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments. [ ]

[ ] SECOND: Sparsely Embedded Convolutional Detection. [ ] [ ]

[ ] Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds). [ ] [ ]

[ ] PCN: Point Completion Network. [ ] [ ]

[ ] A Graph-CNN for 3D Point Cloud Classification. [ ] [ ]

[ ] BirdNet: a 3D Object Detection Framework from LiDAR information. [ ]

[ ] PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation. [ ] [ ]

[ ] Spherical Convolutional Neural Network for 3D Point Clouds. [ ]

[ ] Adversarial Autoencoders for Generating 3D Point Clouds. [ ]

[ ] Iterative Transformer Network for 3D Point Cloud. [ ]

[ ] Topology-Aware Surface Reconstruction for Point Clouds. [ ]

[ ] Inferring Point Clouds from Single Monocular Images by Depth Intermediation. [ ]

[ ] Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions. [ ]

[ ] IPOD: Intensive Point-based Object Detector for Point Cloud. [ ]

[ ] Feature Preserving and Uniformity-controllable Point Cloud Simplification on Graph. [ ]

[ ] POINTCLEANNET: Learning to Denoise and Remove Outliers from Dense Point Clouds. [ ] [ ]

[ ] Complex-YOLO: Real-time 3D Object Detection on Point Clouds. [ ] [ ]

[ ] RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement. [ ] [ ]

[ ] Multi-column Point-CNN for Sketch Segmentation. [ ]

[ ] PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention. [ ] [ ]

[ ] Point Cloud GAN. [ ] [ ]

[ ] Relation-Shape Convolutional Neural Network for Point Cloud Analysis. [ ] [ ]

[ ] Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition. [ ]

[ ] DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds. [ ] [ ]

[ ] Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. [ ] [ ]

[ ] PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. [ ] [ ]

[ ] Generating 3D Adversarial Point Clouds. [ ] [ ]

[ ] Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling. [ ]

[ ] A-CNN: Annularly Convolutional Neural Networks on Point Clouds. [ ][ ]

[ ] PointConv: Deep Convolutional Networks on 3D Point Clouds. [ ] [ ]

[ ] Path-Invariant Map Networks. [ ] [ ]

[ ] PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. [ ] [ ]

[ ] GeoNet: Deep Geodesic Networks for Point Cloud Analysis. [ ]

[ ] Associatively Segmenting Instances and Semantics in Point Clouds. [ ] [ ]

[ ] Supervised Fitting of Geometric Primitives to 3D Point Clouds. [ ] [ ]

[ ] Octree guided CNN with Spherical Kernels for 3D Point Clouds. [ ] [ ] [ ]

[ ] PointNetLK: Point Cloud Registration using PointNet. [ ] [ ]

[ ] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields. [ ] [ ]

[ ] Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. [ ]

[ ] PointPillars: Fast Encoders for Object Detection from Point Clouds. [ ] [ ]

[ ] Patch-based Progressive 3D Point Set Upsampling. [ ] [ ]

[ ] PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval. [ ] [ ]

[ ] PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation. [ ] [ ]

[ ] PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds. [ ] [ ]

[ ] SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences. [ ] [ ]

[ ] Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image. [ ]

[ ] Embodied Question Answering in Photorealistic Environments with Point Cloud Perception. [ ]

[ ] 3D Point-Capsule Networks. [ ] [ ]

[ ] 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. [ ] [ ]

[ ] The Perfect Match: 3D Point Cloud Matching with Smoothed Densities. [ ] [ ]

[ ] FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization. [ ] [ ]

[ ] FlowNet3D: Learning Scene Flow in 3D Point Clouds. [ ]

[ ] Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN. [ ]

[ ] ClusterNet: Deep Hierarchical Cluster Network with Rigorously Rotation-Invariant Representation for Point Cloud Analysis. [ ]

[ ] PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing. [ ] [ ]

[ ] RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion. [ ] [ ]

[ ] PointNetLK: Robust & Efficient Point Cloud Registration using PointNet. [ ] [ ]

[ ] Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes. [ ] [ ]

[ ] Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks. [ ] [ ]

[ ] GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud. [ ]

[ ] Graph Attention Convolution for Point Cloud Semantic Segmentation. [ ]

[ ] Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer. [ ]

[ ] LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. [ ]

[ ] LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks. [ ] [ ]

[ ] Structural Relational Reasoning of Point Clouds. [ ]

[ ] 3DN: 3D Deformation Network. [ ] [ ]

[ ] Privacy Preserving Image-Based Localization. [ ]

[ ] Argoverse: 3D Tracking and Forecasting With Rich Maps.[ ]

[ ] Leveraging Shape Completion for 3D Siamese Tracking. [ ] [ ]

[ ] Attentional PointNet for 3D-Object Detection in Point Clouds. [ ] [ ]

[ ] 3D Local Features for Direct Pairwise Registration. [ ]

[ ] Learning to Sample. [ ] [ ]

[ ] Revealing Scenes by Inverting Structure from Motion Reconstructions. [ ] [ ]

[ ] DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image. [ ] [ ]

[ ] HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds. [ ] [ ]

[ ] Deep Hough Voting for 3D Object Detection in Point Clouds. [ ] [ ] [ ]

[ ] DeepGCNs: Can GCNs Go as Deep as CNNs? [ ] [ ]

[ ] PU-GAN: a Point Cloud Upsampling Adversarial Network. [ ] [ ]

[ ] 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition. [ ]

[ ] PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows. [ ] [ ]

[ ] Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction. [ ]

[ ] SO-HandNet: Self-Organizing Network for 3D Hand Pose Estimation with Semi-supervised Learning. [ ] [ ]

[ ] DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense. [ ]

[ ] Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data. [ ] [ ] [ ]

[ ] KPConv: Flexible and Deformable Convolution for Point Clouds. [ ] [ ]

[ ] ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics. [ ] [ ]

[ ] Point-Based Multi-View Stereo Network. [ ] [ ]

[ ] DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing. [ ] [ ]

[ ] DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration. [ ]

[ ] 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions. [ ] [ ]

[ ] Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. [ ]

[ ] Learning an Effective Equivariant 3D Descriptor Without Supervision. [ ]

[ ] Fully Convolutional Geometric Features. [ ] [ ]

[ ] LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis. [ ]

[ ] Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning. [ ] [ ]

[ ] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds. [ ] [ ]

[ ] Interpolated Convolutional Networks for 3D Point Cloud Understanding. [ ]

[ ] PointCloud Saliency Maps. [ ] [ ]

[ ] STD: Sparse-to-Dense 3D Object Detector for Point Cloud. [ ]

[ ] Accelerated Gravitational Point Set Alignment with Altered Physical Laws. [ ]

[ ] Deep Closest Point: Learning Representations for Point Cloud Registration. [ ]

[ ] Efficient Learning on Point Clouds with Basis Point Sets. [ ] [ ]

[ ] PointAE: Point Auto-encoder for 3D Statistical Shape and Texture Modelling. [ ]

[ ] Skeleton-Aware 3D Human Shape Reconstruction From Point Clouds. [ ]

[ ] Dynamic Points Agglomeration for Hierarchical Point Sets Learning. [ ] [ ]

[ ] Unsupervised Multi-Task Feature Learning on Point Clouds. [ ]

[ ] VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation. [ ] [ ]

[ ] GraphX-Convolution for Point Cloud Deformation in 2D-to-3D Conversion. [ ] [ ]

[ ] MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences. [ ] [ ]

[ ] Fast Point R-CNN. [ ]

[ ] Robust Variational Bayesian Point Set Registration. [ ]

[ ] DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing. [ ]

[ ] Learning an Effective Equivariant 3D Descriptor Without Supervision. [ ]

[ ] 3D Instance Segmentation via Multi-Task Metric Learning. [ ] [ ]

[ ] 3D Face Modeling From Diverse Raw Scan Data. [ ]

[ ] Range Adaptation for 3D Object Detection in LiDAR. [ ]

[ ] Self-Supervised Deep Learning on Point Clouds by Reconstructing Space. [ ]

[ ] Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. [ ] [ ]

[ ] Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations. [ ] [ ]

[ ] Point-Voxel CNN for Efficient 3D Deep Learning. [ ]

[ ] PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. [ ] [ ]

[ ] Learning Localized Generative Models for 3D Point Clouds via Graph Convolution. [ ]

[ ] LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving. [ ]

[ ] CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision. [ ] [ ]

[ ] Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network. [ ] [ ]

[ ] Point Cloud Processing via Recurrent Set Encoding. [ ]

[ ] PVRNet: Point-View Relation Neural Network for 3D Shape Recognition. [ ] [ ]

[ ] Hypergraph Neural Networks. [ ] [ ]

[ ] Dynamic Graph CNN for Learning on Point Clouds. [ ][ ] [ ]

[ ] LOGAN: Unpaired Shape Transform in Latent Overcomplete Space. [ ] [ ]

[ ] RPM-Net: recurrent prediction of motion and parts from point cloud. [ ] [ ]

[ ] StructureNet: Hierarchical Graph Networks for 3D Shape Generation. [ ]

[ ] MMJN: Multi-Modal Joint Networks for 3D Shape Recognition. [ ]

[ ] 3D Point Cloud Geometry Compression on Deep Learning. [ ]

[ ] SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation. [ ] [ ]

[ ] L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention. [ ]

[ ] Ground-Aware Point Cloud Semantic Segmentation for Autonomous Driving. [ ] [ ]

[ ] Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables. [ ]

[ ] 3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation. [ ] [ ]

[ ] Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects. [ ]

[ ] Discrete Rotation Equivariance for Point Cloud Recognition. [ ] [ ]

[ ] SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud. [ ] [ ]

[ ] Detection and Tracking of Small Objects in Sparse 3D Laser Range Data. [ ]

[ ] Oriented Point Sampling for Plane Detection in Unorganized Point Clouds. [ ]

[ ] Point Cloud Compression for 3D LiDAR Sensor Using Recurrent Neural Network with Residual Blocks. [ ] [ ]

[ ] Focal Loss in 3D Object Detection. [ ] [ ]

[ ] PointNetGPD: Detecting Grasp Configurations from Point Sets. [ ] [ ]

[ ] 2D3D-MatchNet: Learning to Match Keypoints across 2D Image and 3D Point Cloud. [ ]

[ ] Speeding up Iterative Closest Point Using Stochastic Gradient Descent. [ ]

[ ] Uncertainty Estimation for Projecting Lidar Points Onto Camera Images for Moving Platforms. [ ]

[ ] SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data. [ ]

[ ] BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving. [ ] [ ]

[ ] A Fast and Robust 3D Person Detector and Posture Estimator for Mobile Robotic Applications. [ ]

[ ] Robust low-overlap 3-D point cloud registration for outlier rejection. [ ] [ ]

[ ] Robust 3D Object Classification by Combining Point Pair Features and Graph Convolution. [ ]

[ ] Hierarchical Depthwise Graph Convolutional Neural Network for 3D Semantic Segmentation of Point Clouds. [ ]

[ ] Robust Generalized Point Set Registration Using Inhomogeneous Hybrid Mixture Models Via Expectation. [ ]

[ ] Dense 3D Visual Mapping via Semantic Simplification. [ ]

[ ] MVX-Net: Multimodal VoxelNet for 3D Object Detection. [ ]

[ ] CELLO-3D: Estimating the Covariance of ICP in the Real World. [ ]

[ ] EPN: Edge-Aware PointNet for Object Recognition from Multi-View 2.5D Point Clouds. [ ] [ ]

[ ] SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles. [ ] [ ]

[ ] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud. [ ]

[ ] End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving. [ ] [ ]

[ ] Generalizing Discrete Convolutions for Unstructured Point Clouds. [ ] [ ]

[ ] 3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds. [ ]

[ ] Rotation Invariant Convolutions for 3D Point Clouds Deep Learning. [ ] [ ]

[ ] Effective Rotation-invariant Point CNN with Spherical Harmonics kernels. [ ] [ ]

[ ] LassoNet: Deep Lasso-Selection of 3D Point Clouds. [ ] [ ]

[ ] Fast 3D Line Segment Detection From Unorganized Point Cloud. [ ]

[ ] Point-Cloud Saliency Maps. [ ] [ ]

[ ] Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers. [ ] [ ]

[ ] Context Prediction for Unsupervised Deep Learning on Point Clouds. [ ]

[ ] Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks. [ ]

[ ] NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler. [ ]

[ ] 3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation. [ ]

[ ] Zero-shot Learning of 3D Point Cloud Objects. [ ] [ ]

[ ] Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. [ ]

[ ] Real-time Multiple People Hand Localization in 4D Point Clouds. [ ]

[ ] Variational Graph Methods for Efficient Point Cloud Sparsification. [ ]

[ ] Neural Style Transfer for Point Clouds. [ ]

[ ] OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios. [ ]

[ ] FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds. [ ] [ ]

[ ] Unpaired Point Cloud Completion on Real Scans using Adversarial Training. [ ]

[ ] MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds. [ ]

[ ] DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds. [ ]

[ ] Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. [ ] [ ]

[ ] Graph-based Inpainting for 3D Dynamic Point Clouds. [ ]

[ ] nuScenes: A multimodal dataset for autonomous driving. [ ] [ ]

[ ] 3D Backbone Network for 3D Object Detection. [ ] [ ]

[ ] Adversarial Autoencoders for Compact Representations of 3D Point Clouds. [ ] [ ]

[ ] Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features. [ ]

[ ] GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud. [ ] [ ]

[ ] Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. [ ] [ ]

[ ] Differentiable Surface Splatting for Point-based Geometry Processing. [ ] [ ]

[ ] Spatial Transformer for 3D Points. [ ]

[ ] Point-Voxel CNN for Efficient 3D Deep Learning. [ ]

[ ] Neural Point-Based Graphics. [ ] [ ]

[ ] Point Cloud Super Resolution with Adversarial Residual Graph Networks. [ ] [ ]

[ ] Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes. [ ]

[ ] StarNet: Targeted Computation for Object Detection in Point Clouds. [ ] [ ]

[ ] Efficient Tracking Proposals using 2D-3D Siamese Networks on LIDAR. [ ]

[ ] SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud Processing. [ ] [ ]

[ ] Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. [ ]

[ ] PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation. [ ]

[ ] PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing. [ ] [ ]

[ ] PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points. [ ] [ ]

[ ] Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds. [ ]

[ ] 3D-Rotation-Equivariant Quaternion Neural Networks. [ ]

[ ] Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules. [ ]

[ ] Geometric Feedback Network for Point Cloud Classification. [ ]

[ ] Relation Graph Network for 3D Object Detection in Point Clouds. [ ]

[ ] Deformable Filter Convolution for Point Cloud Reasoning. [ ]

[ ] PU-GCN: Point Cloud Upsampling via Graph Convolutional Network. [ ] [ ]

[ ] StructEdit: Learning Structural Shape Variations. [ ] [ ]

[ ] Grid-GCN for Fast and Scalable Point Cloud Learning. [ ]

[ ] PointPainting: Sequential Fusion for 3D Object Detection. [ ]

[ ] Transductive Zero-Shot Learning for 3D Point Cloud Classification. [ ]

[ ] Geometry Sharing Network for 3D Point Cloud Classification and Segmentation. [ ] [ ]

[ ] Deep Learning for 3D Point Clouds: A Survey. [ ] [ ]

[ ] Spectral-GANs for High-Resolution 3D Point-cloud Generation. [ ]

[ ] Point Attention Network for Semantic Segmentation of 3D Point Clouds. [ ]

[ ] PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation. [ ]

[ ] 3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models. [ ]

[ ] Morphing and Sampling Network for Dense Point Cloud Completion. [ ] [ ]

[ ] TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. [ ] [ ]

[ ] Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling. [ ]

[ ] PRIN: Pointwise Rotation-Invariant Network. [ ]

[ ] Just Go with the Flow: Self-Supervised Scene Flow Estimation. [ ][ ]

[ ] SGAS: Sequential Greedy Architecture Search. [ ] [ ]

[ ] RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [ ] [ ]

[ ] Learning multiview 3D point cloud registration. [ ] [ ]

[ ] PF-Net: Point Fractal Network for 3D Point Cloud Completion. [ ] [ ]

[ ] MLCVNet: Multi-Level Context VoteNet for 3D Object Detection. [ ] [ ]

[ ] SampleNet: Differentiable Point Cloud Sampling. [ ] [ ]

[ ] MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment. [ ]

[ ] Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. [ ] [ ]

[ ] Attentive Context Normalization for Robust Permutation-Equivariant Learning. [ ] [ ]

[ ] Implicit Functions in Feature Space for Shape Reconstruction and Completion. [ ] [ ]

[ ] PointAugment: an Auto-Augmentation Framework for Point Cloud Classification. [ ]

[ ] FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data. [ ]

[ ] ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes. [ ]

[ ] Quaternion Equivariant Capsule Networks for 3D Point Clouds. [ ]

[ ] PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding. [ ]

[ ] DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. [ ] [ ]

[ ] DPDist: Comparing Point Clouds Using Deep Point Cloud Distance. [ ] [ ]

[ ] GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. [ ] [ ]

[ ] AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing. [ ][ ]

[ ] Parameter-Efficient Person Re-identification in the 3D Space. [ ]

[ ] PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences. [ ]

[ ] Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos. [ ][ ]

[ ] PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds. [ ][ ]

[ ] FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection. [ ][ ]

[ ] MVTN: Multi-View Transformation Network for 3D Shape Recognition. [ ][ ]

[ ] The KITTI Vision Benchmark Suite. [ ]

[ ] The Princeton ModelNet . [ ]

[ ] A collaborative dataset between researchers at Princeton, Stanford and TTIC. [ ]

[ ] The PartNet dataset provides fine grained part annotation of objects in ShapeNetCore. [ ]

[ ] PartNet benchmark from Nanjing University and National University of Defense Technology. [ ]

[ ] The Stanford Large-Scale 3D Indoor Spaces Dataset. [ ]

[ ] Richly-annotated 3D Reconstructions of Indoor Scenes. [ ]

[ ] The Stanford 3D Scanning Repository. [ ]

[ ] . [ ]

[ ] The Princeton Shape Benchmark

[ ] This dataset contains a variety of common urban road objects scanned with a Velodyne HDL-64E LIDAR, collected in the CBD of Sydney, Australia. There are 631 individual scans of objects across classes of vehicles, pedestrians, signs and trees. [ ]

[ ] This site is dedicated to provide datasets for the Robotics community with the aim to facilitate result evaluations and comparisons. [ ]

[ ] This benchmark closes the gap and provides a large labelled 3D point cloud data set of natural scenes with over 4 billion points in total. [ ]

[ ] The Canadian Planetary Emulation Terrain 3D Mapping Dataset is a collection of three-dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada

[ ] The Robotics Data Set Repository (Radish for short) provides a collection of standard robotics data sets

[ ] The database contains 3D MLS data from a dense urban environment in Paris (France), composed of 300 million points. The acquisition was made in January 2013. [ ]

[ ] This repository contains labeled 3-D point cloud laser data collected from a moving platform in a urban environment

[ ] This repository provides 3D point clouds from robotic experiments，log files of robot runs and standard 3D data sets for the robotics community

[ ] The dataset is collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck

[ ] This dataset contains about 14,000 labeled tracks of objects as observed in natural street scenes by a Velodyne HDL-64E S2 LIDAR

[ ] Beyond PASCAL: A Benchmark for 3D Object Detection in the Wild. [ ]

[ ] The aim of this dataset is to provide a simple way to get started with 3D computer vision problems such as 3D shape recognition. [ ]

[ ] [ ] The datasets are provided by Baidu Inc. [ ]

[ ] The nuScenes dataset is a large-scale autonomous driving dataset

[ ] Depth information, semantic segmentation (images), point-wise segmentation (point clouds), ground point labels (point clouds), and detailed annotations for all vehicles and people. [ ] [ ]

[ ] Keypoint Matching Benchmark, Geometric Registration Benchmark, RGB-D Reconstruction Datasets. [ ]

BLVD

[ ] (a) 3D detection, (b) 4D tracking, (c) 5D interactive event recognition and (d) 5D intention prediction. [ ] [ ]

[ ] 3D Pose Estimation of Pedestrians, more than 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing 2D and 3D labels of pedestrians. [ ] [ ]

[ ] Full-surround 3D multi-object detection and tracking dataset. [ ] [ ]

Two public datasets (3D Tracking and Motion Forecasting) supported by highly detailed maps to test, experiment, and teach self-driving vehicles how to understand the world around them.[ ][ ]

[ ] RGB-D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [ ] [ ] [ ]

[ ] SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Nine categories. [ ]

[ ] Include high quality, human-labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map. [ ]

[ ] Sequential Semantic Segmentation, 28 classes, for autonomous driving. All sequences of KITTI odometry labeled. [ ] [ ]

[ ] The Paris-Lille-3D has been produced by a Mobile Laser System (MLS) in two different cities in France (Paris and Lille). [ ]

[ ] The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions. [ ]

A*3D: An Autonomous Driving Dataset in Challeging Environments

[ ] A*3D: An Autonomous Driving Dataset in Challeging Environments. [ ]

PointDA-10 Dataset

[ ] Domain Adaptation for point clouds

[ ] The dataset captures many different combinations of weather, traffic and pedestrians. [ ]

[ ] Public large-scale dataset for autonomous driving provided by Hesai & Scale. It enables researchers to study challenging urban driving situations using the full sensor suit of a real self-driving-car. [ ]

[ ] [Alibaba] 3D-FRONT contains 10,000 houses (or apartments) and ~70,000 rooms with layout information. 3D-FUTURE contains 20,000+ clean and realistic synthetic scenes in 5,000+ diverse rooms which contain 10,000+ unique high quality 3D instances of furniture

[ ] The Campus3D contains a photogrametry point cloud which has 931.7 million points, covering 1.58 km2 of 6 connected campus regions of NUS. The dataset are point-wisely annotated with a hierarchical structure of 24 semantic labels and contains 2,530 instances based on the labels. [ ][ ][ ]