点云论文汇总

本博客转自https://github.com/Yochengliu/awesome-point-cloud-analysis

awesome-point-cloud-analysis

for anyone who wants to do research about 3D point cloud.

- Recent papers (from 2017)

Keywords

dat.: dataset   |   cls.: classification   |   rel.: retrieval   |   seg.: segmentation
det.: detection   |   tra.: tracking   |   pos.: pose   |   dep.: depth
reg.: registration   |   rec.: reconstruction   |   aut.: autonomous driving
oth.: other, including normal-related, correspondence, mapping, matching, alignment, compression, generative model…

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2017

• [CVPR] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [tensorflow][pytorch] [cls. seg. det.] ⭐️
• [CVPR] Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. [cls.] ⭐️
• [CVPR] SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation. [torch] [seg. oth.] ⭐️
• [CVPR] ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. [project][git] [dat. cls. rel. seg. oth.] ⭐️
• [CVPR] Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity. [oth.]
• [CVPR] Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures. [code] [oth.]
• [CVPR] Discriminative Optimization: Theory and Applications to Point Cloud Registration. [reg.]
• [CVPR] 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder. [git] [reg.]
• [CVPR] Multi-View 3D Object Detection Network for Autonomous Driving. [tensorflow] [det. aut.] ⭐️
• [CVPR] 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions. [code] [dat. pos. reg. rec. oth.] ⭐️
• [ICCV] Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. [pytorch] [cls. rel. seg.] ⭐️
• [ICCV] 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds. [code] [seg.]
• [ICCV] Colored Point Cloud Registration Revisited. [reg.]
• [ICCV] PolyFit: Polygonal Surface Reconstruction from Point Clouds. [code] [rec.]
• [ICCV] From Point Clouds to Mesh using Regression. [rec.]
• [ICCV] 3D Graph Neural Networks for RGBD Semantic Segmentation. [pytorch] [seg.]
• [NeurIPS] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. [tensorflow][pytorch] [cls. seg.] ⭐️
• [NeurIPS] Deep Sets. [pytorch] [cls.] ⭐️
• [ICRA] Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. [code] [det. aut.] ⭐️
• [ICRA] Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. [code] [seg. aut.]
• [ICRA] SegMatch: Segment based place recognition in 3D point clouds. [seg. oth.]
• [ICRA] Using 2 point+normal sets for fast registration of point clouds with small overlap. [reg.]
• [IROS] Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework. [det. aut.]
• [IROS] 3D object classification with point convolution network. [cls.]
• [IROS] 3D fully convolutional network for vehicle detection in point cloud. [tensorflow] [det. aut.] ⭐️
• [IROS] Deep learning of directional truncated signed distance function for robust 3D object recognition. [det. pos.]
• [IROS] Analyzing the quality of matched 3D point clouds of objects. [oth.]
• [3DV] SEGCloud: Semantic Segmentation of 3D Point Clouds. [project] [seg. aut.] ⭐️
• [TPAMI] Structure-aware Data Consolidation. [oth.]

---

2018

• [CVPR] SPLATNet: Sparse Lattice Networks for Point Cloud Processing. [caffe] [seg.]
• [CVPR] Attentional ShapeContextNet for Point Cloud Recognition. [cls. seg.]
• [CVPR] Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling. [code] [cls. seg.]
• [CVPR] FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation. [code] [cls.]
• [CVPR] Pointwise Convolutional Neural Networks. [tensorflow] [cls. seg.]
• [CVPR] PU-Net: Point Cloud Upsampling Network. [tensorflow] [rec. oth.]
• [CVPR] SO-Net: Self-Organizing Network for Point Cloud Analysis. [pytorch] [cls. seg.] ⭐️
• [CVPR] Recurrent Slice Networks for 3D Segmentation of Point Clouds. [pytorch] [seg.]
• [CVPR] 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. [pytorch] [seg.]
• [CVPR] Deep Parametric Continuous Convolutional Neural Networks. [seg. aut.]
• [CVPR] PIXOR: Real-time 3D Object Detection from Point Clouds. [pytorch] [det. aut.]
• [CVPR] SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation. [tensorflow] [seg.]
• [CVPR] Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. [pytorch] [seg.]
• [CVPR] VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. [tensorflow] [det. aut.] ⭐️
• [CVPR] Reflection Removal for Large-Scale 3D Point Clouds. [oth.]
• [CVPR] Hand PointNet: 3D Hand Pose Estimation using Point Sets. [pytorch] [pos.]
• [CVPR] PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition. [tensorflow] [rel.]
• [CVPR] A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation. [cls.]
• [CVPR] Density Adaptive Point Set Registration. [code] [reg.]
• [CVPR] A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds. [seg.]
• [CVPR] Inverse Composition Discriminative Optimization for Point Cloud Registration. [reg.]
• [CVPR] CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles. [tra. det. rec.]
• [CVPR] PPFNet: Global Context Aware Local Features for Robust 3D Point Matching. [oth.]
• [CVPR] PointGrid: A Deep Network for 3D Shape Understanding. [tensorflow] [cls. seg.]
• [CVPR] PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation. [code] [det. aut.]
• [CVPR] Frustum PointNets for 3D Object Detection from RGB-D Data. [tensorflow] [det. aut.] ⭐️
• [CVPR] Tangent Convolutions for Dense Prediction in 3D. [tensorflow] [seg. aut.]
• [ECCV] Multiresolution Tree Networks for 3D Point Cloud Processing. [pytorch] [cls.]
• [ECCV] EC-Net: an Edge-aware Point set Consolidation Network. [tensorflow] [oth.]
• [ECCV] 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation. [seg.]
• [ECCV] Learning and Matching Multi-View Descriptors for Registration of Point Clouds. [reg.]
• [ECCV] 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. [tensorflow] [reg.]
• [ECCV] Local Spectral Graph Convolution for Point Set Feature Learning. [tensorflow] [cls. seg.]
• [ECCV] SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. [tensorflow] [cls. seg.]
• [ECCV] Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search. [reg.]
• [ECCV] Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields. [rec.]
• [ECCV] Fully-Convolutional Point Networks for Large-Scale Point Clouds. [tensorflow] [seg. oth.]
• [ECCV] Deep Continuous Fusion for Multi-Sensor 3D Object Detection. [det.]
• [ECCV] HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration. [reg.]
• [ECCV] Point-to-Point Regression PointNet for 3D Hand Pose Estimation. [pos.]
• [AAAI] Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction. [tensorflow] [rec.]
• [AAAI] Adaptive Graph Convolutional Neural Networks. [cls.]
• [NeurIPS] Unsupervised Learning of Shape and Pose with Differentiable Point Clouds. [tensorflow] [pos.]
• [NeurIPS] PointCNN: Convolution On X-Transformed Points. [tensorflow][pytorch] [cls. seg.]
• [SIGGRAPH] Point Convolutional Neural Networks by Extension Operators. [tensorflow] [cls. seg.]
• [SIGGRAPH] P2P-NET: Bidirectional Point Displacement Net for Shape Transform. [tensorflow] [oth.]
• [SIGGRAPH Asia] Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. [tensorflow] [cls. seg. oth.]
• [SIGGRAPH] Learning local shape descriptors from part correspondences with multi-view convolutional networks. [project] [seg. oth.]
• [MM] PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition. [cls. rel.]
• [MM] RGCNN: Regularized Graph CNN for Point Cloud Segmentation. [tensorflow] [seg.]
• [MM] Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction. [oth.]
• [ICRA] End-to-end Learning of Multi-sensor 3D Tracking by Detection. [det. tra. aut.]
• [ICRA] Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping. [seg. oth.]
• [ICRA] SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. [tensorflow] [seg. aut.]
• [ICRA] Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications. [det.]
• [ICRA] High-Precision Depth Estimation with the 3D LiDAR and Stereo Fusion. [dep. aut.]
• [ICRA] Sampled-Point Network for Classification of Deformed Building Element Point Clouds. [cls.]
• [ICRA] Gemsketch: Interactive Image-Guided Geometry Extraction from Point Clouds. [oth.]
• [ICRA] Signature of Topologically Persistent Points for 3D Point Cloud Description. [oth.]
• [ICRA] A General Pipeline for 3D Detection of Vehicles. [det. aut.]
• [ICRA] Robust and Fast 3D Scan Alignment Using Mutual Information. [oth.]
• [ICRA] Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities. [oth.]
• [ICRA] Surface-Based Exploration for Autonomous 3D Modeling. [oth. aut.]
• [ICRA] Deep Lidar CNN to Understand the Dynamics of Moving Vehicles. [oth. aut.]
• [ICRA] Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. [oth.]
• [ICRA] Real-Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation. [tra.]
• [ICRA] Robust Generalized Point Cloud Registration Using Hybrid Mixture Model. [reg.]
• [ICRA] A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration. [reg.]
• [ICRA] Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping. [oth.]
• [ICRA] Direct Visual SLAM Using Sparse Depth for Camera-LiDAR System. [oth.]
• [ICRA] Spatiotemporal Learning of Dynamic Gestures from 3D Point Cloud Data. [cls.]
• [ICRA] Asynchronous Multi-Sensor Fusion for 3D Mapping and Localization. [oth.]
• [ICRA] Complex Urban LiDAR Data Set. [video] [dat. oth.]
• [IROS] CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks.[tensorflow] [oth. aut.]
• [IROS] Dynamic Scaling Factors of Covariances for Accurate 3D Normal Distributions Transform Registration. [reg.]
• [IROS] A 3D Laparoscopic Imaging System Based on Stereo-Photogrammetry with Random Patterns. [rec. oth.]
• [IROS] Robust Generalized Point Cloud Registration with Expectation Maximization Considering Anisotropic Positional Uncertainties. [reg.]
• [IROS] Octree map based on sparse point cloud and heuristic probability distribution for labeled images. [oth. aut.]
• [IROS] PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization. [oth.]
• [IROS] Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map. [oth.]
• [IROS] LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.[code] [pos. oth.]
• [IROS] Classification of Hanging Garments Using Learned Features Extracted from 3D Point Clouds. [cls.]
• [IROS] Stereo Camera Localization in 3D LiDAR Maps. [pos. oth.]
• [IROS] Joint 3D Proposal Generation and Object Detection from View Aggregation. [det.] ⭐️
• [IROS] Joint Point Cloud and Image Based Localization for Efficient Inspection in Mixed Reality. [oth.]
• [IROS] Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding. [det. oth.]
• [IROS] NDVI Point Cloud Generator Tool Using Low-Cost RGB-D Sensor. [code][oth.]
• [IROS] A 3D Convolutional Neural Network Towards Real-Time Amodal 3D Object Detection. [det. pos.]
• [IROS] Extracting Phenotypic Characteristics of Corn Crops Through the Use of Reconstructed 3D Models. [seg. rec.]
• [IROS] PCAOT: A Manhattan Point Cloud Registration Method Towards Large Rotation and Small Overlap. [reg.]
• [SENSORS] SECOND: Sparsely Embedded Convolutional Detection. [pytorch] [det. aut.]
• [ACCV] Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds). [tensorflow] [seg.]
• [3DV] PCN: Point Completion Network. [tensorflow] [reg. oth. aut.]
• [ICASSP] A Graph-CNN for 3D Point Cloud Classification. [tensorflow] [cls.]
• [arXiv] PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation. [tensorflow] [seg.]
• [arXiv] Spherical Convolutional Neural Network for 3D Point Clouds. [cls.]
• [arXiv] Adversarial Autoencoders for Generating 3D Point Clouds. [oth.]
• [arXiv] Iterative Transformer Network for 3D Point Cloud. [cls. seg. pos.]
• [arXiv] Topology-Aware Surface Reconstruction for Point Clouds. [rec.]
• [arXiv] Inferring Point Clouds from Single Monocular Images by Depth Intermediation. [oth.]
• [arXiv] Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions. [cls.]
• [arXiv] IPOD: Intensive Point-based Object Detector for Point Cloud. [det.]
• [arXiv] 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition. [rel. oth.]
• [arXiv] Deflecting 3D Adversarial Point Clouds Through Outlier-Guided Removal. [oth.]
• [arXiv] Feature Preserving and Uniformity-controllable Point Cloud Simplification on Graph. [oth.]
• [arXiv] POINTCLEANNET: Learning to Denoise and Remove Outliers from Dense Point Clouds. [pytorch] [oth.]
• [arXiv] Complex-YOLO: Real-time 3D Object Detection on Point Clouds. [pytorch] [det. aut.]
• [arxiv] RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement. [tensorflow] [det. aut.]
• [arXiv] Multi-column Point-CNN for Sketch Segmentation. [seg.]

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2019

• [CVPR] Relation-Shape Convolutional Neural Network for Point Cloud Analysis. [pytorch] [cls. seg. oth.]
• [CVPR] Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition. [cls. seg.]
• [CVPR] DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds. [code] [reg.]
• [CVPR] Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. [code] [det. dep. aut.]
• [CVPR] PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. [pytorch] [det. aut.]
• [CVPR] Generating 3D Adversarial Point Clouds. [code] [oth.]
• [CVPR] Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling. [cls. seg.]
• [CVPR] A-CNN: Annularly Convolutional Neural Networks on Point Clouds. [cls. seg.]
• [CVPR] PointConv: Deep Convolutional Networks on 3D Point Clouds. [tensorflow] [cls. seg.]
• [CVPR] Path-Invariant Map Networks. [tensorflow] [seg. oth.]
• [CVPR] PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. [code] [dat. seg.]
• [CVPR] GeoNet: Deep Geodesic Networks for Point Cloud Analysis. [cls. rec. oth.]
• [CVPR] Associatively Segmenting Instances and Semantics in Point Clouds. [tensorflow] [seg.]
• [CVPR] Supervised Fitting of Geometric Primitives to 3D Point Clouds. [tensorflow] [oth.]
• [CVPR] Octree guided CNN with Spherical Kernels for 3D Point Clouds. [cls. seg.]
• [CVPR] PointNetLK: Point Cloud Registration using PointNet. [pytorch] [reg.]
• [CVPR] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields. [pytorch] [seg.]
• [CVPR] Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. [seg.]
• [CVPR] PointPillars: Fast Encoders for Object Detection from Point Clouds. [pytorch] [det.]
• [CVPR] Patch-based Progressive 3D Point Set Upsampling. [tensorflow] [oth.]
• [CVPR] PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval. [code] [rel.]
• [CVPR] PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation. [pytorch] [dat. seg.]
• [CVPR] PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds. [code] [det. dat. oth.]
• [CVPR] SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences. [matlab] [reg.]
• [CVPR] Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image. [rec. oth.]
• [CVPR] Embodied Question Answering in Photorealistic Environments with Point Cloud Perception. [oth.]
• [CVPR] 3D Point-Capsule Networks. [pytorch] [cls. rec. oth.]
• [CVPR] 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. [seg.]
• [CVPR] The Perfect Match: 3D Point Cloud Matching with Smoothed Densities. [tensorflow] [oth.]
• [CVPR] FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization. [code] [reg.]
• [CVPR] FlowNet3D: Learning Scene Flow in 3D Point Clouds. [oth.]
• [CVPR] Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN. [cls. det.]
• [CVPR] ClusterNet: Deep Hierarchical Cluster Network with Rigorously Rotation-Invariant Representation for Point Cloud Analysis. [cls.]
• [CVPR] PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing. [cls. seg.]
• [CVPR] RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion. [code] [oth.]
• [CVPR] PointNetLK: Robust & Efficient Point Cloud Registration using PointNet. [pytorch] [reg.]
• [CVPR] Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes. [code] [rec.]
• [CVPR] Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks. [tensorflow] [oth.]
• [CVPR] GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud. [seg.]
• [CVPR] Graph Attention Convolution for Point Cloud Segmentation. [seg.]
• [CVPR] Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer. [pos.]
• [CVPR] LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. [det. aut.]
• [CVPR] LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks. [project] [cls. seg.]
• [CVPR] Structural Relational Reasoning of Point Clouds. [cls. seg.]
• [CVPR] 3DN: 3D Deformation Network. [tensorflow] [rec. oth.]
• [CVPR] Privacy Preserving Image-Based Localization. [pos. oth.]
• [CVPR] Argoverse: 3D Tracking and Forecasting With Rich Maps.[tra. aut.]
• [CVPRW] Attentional PointNet for 3D-Object Detection in Point Clouds. [pytorch] [cls. det. aut.]
• [ICLR] Learning Localized Generative Models for 3D Point Clouds via Graph Convolution. [oth.]
• [AAAI] CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision. [code] [rec.]
• [AAAI] Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network. [tensorflow] [cls. seg.]
• [AAAI] Point Cloud Processing via Recurrent Set Encoding. [cls.]
• [AAAI] PVRNet: Point-View Relation Neural Network for 3D Shape Recognition. [pytorch] [cls. rel.]
• [AAAI] Hypergraph Neural Networks. [pytorch] [cls.]
• [TOG] Dynamic Graph CNN for Learning on Point Clouds. [tensorflow][pytorch] [cls. seg.] ⭐️
• [ICRA] Discrete Rotation Equivariance for Point Cloud Recognition. [pytorch] [cls.]
• [ICRA] SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud. [tensorflow] [seg. aut.]
• [ICRA] Detection and Tracking of Small Objects in Sparse 3D Laser Range Data. [det. tra. aut.]
• [ICRA] Oriented Point Sampling for Plane Detection in Unorganized Point Clouds. [det. seg.]
• [ICRA] Point Cloud Compression for 3D LiDAR Sensor Using Recurrent Neural Network with Residual Blocks. [pytorch] [oth.]
• [ICRA] Focal Loss in 3D Object Detection. [code] [det. aut.]
• [ICRA] PointNetGPD: Detecting Grasp Configurations from Point Sets. [pytorch] [det. seg.]
• [ICRA] 2D3D-MatchNet: Learning to Match Keypoints across 2D Image and 3D Point Cloud. [oth.]
• [ICRA] Speeding up Iterative Closest Point Using Stochastic Gradient Descent. [oth.]
• [ICRA] Uncertainty Estimation for Projecting Lidar Points Onto Camera Images for Moving Platforms. [oth.]
• [ICRA] SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data. [det. aut.]
• [ICRA] BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving. [project] [dat. det. tra. aut. oth.]
• [ICRA] A Fast and Robust 3D Person Detector and Posture Estimator for Mobile Robotic Applications. [det.]
• [ICRA] Robust low-overlap 3-D point cloud registration for outlier rejection. [matlab] [reg.]
• [ICRA] Robust 3D Object Classification by Combining Point Pair Features and Graph Convolution. [cls. seg.]
• [ICRA] Hierarchical Depthwise Graph Convolutional Neural Network for 3D Semantic Segmentation of Point Clouds. [seg.]
• [ICRA] Robust Generalized Point Set Registration Using Inhomogeneous Hybrid Mixture Models Via Expectation. [reg.]
• [ICRA] Dense 3D Visual Mapping via Semantic Simplification. [oth.]
• [ICRA] MVX-Net: Multimodal VoxelNet for 3D Object Detection. [det. aut.]
• [ICRA] CELLO-3D: Estimating the Covariance of ICP in the Real World. [reg.]
• [Eurographics Workshop] Generalizing Discrete Convolutions for Unstructured Point Clouds. [pytorch] [cls. seg.]
• [arXiv] Fast 3D Line Segment Detection From Unorganized Point Cloud. [det.]
• [arXiv] Point-Cloud Saliency Maps. [tensorflow] [cls. oth.]
• [arXiv] Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers. [code] [oth.]
• [arxiv] Context Prediction for Unsupervised Deep Learning on Point Clouds. [cls. seg.]
• [arXiv] Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks. [oth.]
• [arXiv] NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler. [cls. oth.]
• [arXiv] 3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation. [seg.]
• [arXiv] Zero-shot Learning of 3D Point Cloud Objects. [code] [cls.]
• [arXiv] Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. [det. aut.]
• [arXiv] Real-time Multiple People Hand Localization in 4D Point Clouds. [det. oth.]
• [arXiv] Variational Graph Methods for Efficient Point Cloud Sparsification. [oth.]
• [arXiv] Neural Style Transfer for Point Clouds. [oth.]
• [arXiv] OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios. [pos. oth.]
• [arXiv] FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds. [code] [det. aut.]
• [arXiv] Unpaired Point Cloud Completion on Real Scans using Adversarial Training. [oth.]
• [arXiv] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds. [code] [oth.]
• [arXiv] MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds. [cls. seg.]
• [arXiv] DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds. [cls. rel. oth.]
• [arXiv] A Dataset for Semantic Segmentation of Point Cloud Sequences. [dat. seg.]
• [arXiv] 3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation. [code] [oth.]
• [arXiv] Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. [pytorch] [det. tra. aut.]
• [arXiv] Graph-based Inpainting for 3D Dynamic Point Clouds. [oth.]
• [arXiv] nuScenes: A multimodal dataset for autonomous driving. [link] [dat. det. tra. aut.]
• [arXiv] 3D Backbone Network for 3D Object Detection. [code] [det. aut.]
• [arXiv] KPConv: Flexible and Deformable Convolution for Point Clouds. [tensorflow] [cls. seg.]
• [arXiv] Deep Hough Voting for 3D Object Detection in Point Clouds. [det.]
• [arXiv] Adversarial Autoencoders for Compact Representations of 3D Point Clouds. [pytorch] [rel. oth.]
• [arXiv] Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features. [cls. seg.]
• [arXiv] GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud. [tensorflow] [cls. seg.]
• [arXiv] Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. [tensorflow] [det. seg.]
• [arXiv] Differentiable Surface Splatting for Point-based Geometry Processing. [pytorch] [oth.]
• [arXiv] Spatial Transformer for 3D Points. [seg.]
• [arXiv] Point-Voxel CNN for Efficient 3D Deep Learning. [seg. det. aut.]
• [IV2019] End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving. [seg.] [aut.]

- Datasets

• [KITTI] The KITTI Vision Benchmark Suite. [det.]
• [ModelNet] The Princeton ModelNet . [cls.]
• [ShapeNet] A collaborative dataset between researchers at Princeton, Stanford and TTIC. [seg.]
• [PartNet] The PartNet dataset provides fine grained part annotation of objects in ShapeNetCore. [seg.]
• [PartNet] PartNet benchmark from Nanjing University and National University of Defense Technology. [seg.]
• [S3DIS] The Stanford Large-Scale 3D Indoor Spaces Dataset. [seg.]
• [ScanNet] Richly-annotated 3D Reconstructions of Indoor Scenes. [cls. seg.]
• [Stanford 3D] The Stanford 3D Scanning Repository. [reg.]
• [UWA Dataset] . [cls. seg. reg.]
• [Princeton Shape Benchmark] The Princeton Shape Benchmark.
• [SYDNEY URBAN OBJECTS DATASET] 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. [cls. match.]
• [ASL Datasets Repository(ETH)] This site is dedicated to provide datasets for the Robotics community with the aim to facilitate result evaluations and comparisons. [cls. match. reg. det]
• [Large-Scale Point Cloud Classification Benchmark(ETH)] 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. [cls.]
• [Robotic 3D Scan Repository] 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.
• [Radish] The Robotics Data Set Repository (Radish for short) provides a collection of standard robotics data sets.
• [IQmulus & TerraMobilita Contest] 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. [cls. seg. det.]
• [Oakland 3-D Point Cloud Dataset] This repository contains labeled 3-D point cloud laser data collected from a moving platform in a urban environment.
• [Robotic 3D Scan Repository] This repository provides 3D point clouds from robotic experiments，log files of robot runs and standard 3D data sets for the robotics community.
• [Ford Campus Vision and Lidar Data Set] The dataset is collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck.
• [The Stanford Track Collection] This dataset contains about 14,000 labeled tracks of objects as observed in natural street scenes by a Velodyne HDL-64E S2 LIDAR.
• [PASCAL3D+] Beyond PASCAL: A Benchmark for 3D Object Detection in the Wild. [pos. det.]
• [3D MNIST] The aim of this dataset is to provide a simple way to get started with 3D computer vision problems such as 3D shape recognition. [cls.]
• [WAD] This dataset is provided by Baidu Inc.
• [nuScenes] The nuScenes dataset is a large-scale autonomous driving dataset.
• [PreSIL] Depth information, semantic segmentation (images), point-wise segmentation (point clouds), ground point labels (point clouds), and detailed annotations for all vehicles and people. [paper] [det. aut.]
• [3D Match] Keypoint Matching Benchmark, Geometric Registration Benchmark, RGB-D Reconstruction Datasets. [reg. rec. oth.]
• [BLVD] (a) 3D detection, (b) 4D tracking, © 5D interactive event recognition and (d) 5D intention prediction. [ICRA 2019 paper] [det. tra. aut. oth.]
• [PedX] 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. [ICRA 2019 paper] [pos. aut.]
• [H3D] Full-surround 3D multi-object detection and tracking dataset. [ICRA 2019 paper] [det. tra. aut.]
• [Argoverse BY ARGO AI] 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.[CVPR 2019 paper][tra. aut.]
• [Matterport3D] 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. [3DV 2017 paper] [code] [blog]