for anyone who wants to do research about 3D point cloud.
I will try to update this list everyday!!!
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...
Statistics: 🔥 code is available & stars >= 100 | ⭐ citation >= 50
- [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.] 🔥 ⭐ - [CVPR] OctNet: Learning Deep 3D Representations at High Resolutions. [torch] [
cls.seg.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.]
- [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.] - [ECCV] PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. [
oth.] - [ECCVW] 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues. [
cls.seg.] - [ECCVW] YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud. [
det.aut.] - [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.] 🔥 - [ICML] Learning Representations and Generative Models for 3D Point Clouds. [code] [
oth.] 🔥 - [TOG] 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.] - [IROS] [Tensorflow]3DmFV: Point Cloud Classification and segmentation for unstructured 3D point clouds. [
cls.] - [IROS] Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments. [
oth.] - [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.] 🔥 - [ITSC] BirdNet: a 3D Object Detection Framework from LiDAR information. [
det.aut.] - [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] 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.] - [arXiv] PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention. [project] [
oth.] - [arXiv] Point Cloud GAN. [pytorch] [
oth.]
- [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. [tensorflow][
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. [extension] [code] [
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. [pytorch] [
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. [pytorch] [
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 Semantic 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.] - [CVPR] Leveraging Shape Completion for 3D Siamese Tracking. [pytorch] [
tra.] - [CVPRW] Attentional PointNet for 3D-Object Detection in Point Clouds. [pytorch] [
cls.det.aut.] - [CVPR] 3D Local Features for Direct Pairwise Registration. [
reg.] - [CVPR] Learning to Sample. [tensorflow] [
cls.rec.] - [CVPR] Revealing Scenes by Inverting Structure from Motion Reconstructions. [code] [
rec.] - [CVPR] DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image. [pytorch] [
dep.] - [CVPR] HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds. [pytorch] [
oth.] - [ICCV] Deep Hough Voting for 3D Object Detection in Point Clouds. [pytorch] [tensorflow] [
det.] 🔥 - [ICCV] DeepGCNs: Can GCNs Go as Deep as CNNs? [tensorflow] [pytorch] [
seg.] 🔥 - [ICCV] PU-GAN: a Point Cloud Upsampling Adversarial Network. [tensorflow] [
oth.] - [ICCV] 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition. [
rel.oth.] - [ICCV] PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows. [pytorch] [
oth.] - [ICCV] Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction. [
oth.] - [ICCV] SO-HandNet: Self-Organizing Network for 3D Hand Pose Estimation with Semi-supervised Learning. [code] [
pos.] - [ICCV] DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense. [
oth.] - [ICCV] Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data. [
cls.dat.] [code] [dataset] - [ICCV] KPConv: Flexible and Deformable Convolution for Point Clouds. [tensorflow] [
cls.seg.] 🔥 - [ICCV] ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics. [project] [
seg.] - [ICCV] Point-Based Multi-View Stereo Network. [pytorch] [
rec.] - [ICCV] DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing. [pytorch] [
cls.seg.oth.] - [ICCV] DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration. [
reg.] - [ICCV] 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions. [pytorch] [
oth.] - [ICCV] Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. [
seg.] - [ICCV] Learning an Effective Equivariant 3D Descriptor Without Supervision. [
oth.] - [ICCV] Fully Convolutional Geometric Features. [pytorch] [
reg.] - [ICCV] LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis. [
oth.aut.] - [ICCV] Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning. [tensorflow] [
oth.] - [ICCV] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds. [pytorch] [
oth.] - [ICCV] Interpolated Convolutional Networks for 3D Point Cloud Understanding. [
cls.seg.] - [ICCV] PointCloud Saliency Maps. [code] [
oth.] - [ICCV] STD: Sparse-to-Dense 3D Object Detector for Point Cloud. [
det.oth.] - [ICCV] Accelerated Gravitational Point Set Alignment with Altered Physical Laws. [
reg.] - [ICCV] Deep Closest Point: Learning Representations for Point Cloud Registration. [
reg.] - [ICCV] Efficient Learning on Point Clouds with Basis Point Sets. [code] [
cls.reg.] - [ICCV] PointAE: Point Auto-encoder for 3D Statistical Shape and Texture Modelling. [
rec.] - [ICCV] Skeleton-Aware 3D Human Shape Reconstruction From Point Clouds. [
rec.] - [ICCV] Dynamic Points Agglomeration for Hierarchical Point Sets Learning. [pytorch] [
cls.seg.] - [ICCV] Unsupervised Multi-Task Feature Learning on Point Clouds. [
cls.seg.] - [ICCV] VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation. [tensorflow] [
seg.] - [ICCV] GraphX-Convolution for Point Cloud Deformation in 2D-to-3D Conversion. [pytorch] [
rec.] - [ICCV] MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences. [code] [
cls.seg.oth.] - [ICCV] Fast Point R-CNN. [
det.aut.] - [ICCV] Robust Variational Bayesian Point Set Registration. [
reg.] - [ICCV] DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing. [
rec.oth.] - [ICCV] Learning an Effective Equivariant 3D Descriptor Without Supervision. [
oth.] - [ICCV] 3D Instance Segmentation via Multi-Task Metric Learning. [code] [
seg.] - [ICCV] 3D Face Modeling From Diverse Raw Scan Data. [
rec.] - [ICCVW] Range Adaptation for 3D Object Detection in LiDAR. [
det.aut.] - [NeurIPS] Self-Supervised Deep Learning on Point Clouds by Reconstructing Space. [
cls.oth.] - [NeurIPS] Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. [tensorflow] [
det.seg.] - [NeurIPS] Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations. [tensorflow] [
seg.] - [NeurIPS] Point-Voxel CNN for Efficient 3D Deep Learning. [
det.seg.aut.] - [NeurIPS] PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. [code] [
cls.oth.] - [ICLR] Learning Localized Generative Models for 3D Point Clouds via Graph Convolution. [
oth.] - [ICMLW] LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving. [
det.oth.aut.] - [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.] 🔥 ⭐ - [TOG] LOGAN: Unpaired Shape Transform in Latent Overcomplete Space. [tensorflow] [
oth.] - [SIGGRAPH Asia] StructureNet: Hierarchical Graph Networks for 3D Shape Generation. [
seg.oth.] - [MM] MMJN: Multi-Modal Joint Networks for 3D Shape Recognition. [
cls.rel.] - [MM] 3D Point Cloud Geometry Compression on Deep Learning. [
oth.] - [MM] SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation. [tensorflow] [
cls.seg.] - [MM] L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention. [
cls.rel.] - [MM] Ground-Aware Point Cloud Semantic Segmentation for Autonomous Driving. [code] [
seg.aut.] - [ICME] Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables. [
cls.rel.] - [ICASSP] 3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation. [code] [
oth.] - [BMVC] Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects. [
cls.] - [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.] - [IROS] EPN: Edge-Aware PointNet for Object Recognition from Multi-View 2.5D Point Clouds. [tensorflow] [
cls.det.] - [IROS] SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles. [
oth.] [aut.] - [IROS] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud. [
seg.aut.] - [IV] End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving. [
seg.] [aut.] - [Eurographics Workshop] Generalizing Discrete Convolutions for Unstructured Point Clouds. [pytorch] [
cls.seg.] - [WACV] 3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds. [
cls.] - [3DV] Rotation Invariant Convolutions for 3D Point Clouds Deep Learning. [project] [
cls.seg.] - [3DV] Effective Rotation-invariant Point CNN with Spherical Harmonics kernels. [tensorflow] [
cls.seg.oth.] - [TVCG] LassoNet: Deep Lasso-Selection of 3D Point Clouds. [project] [
oth.] - [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] 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] 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] 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.] - [arXiv] Attentive Context Normalization for Robust Permutation-Equivariant Learning. [
cls.] - [arXiv] Neural Point-Based Graphics. [project] [
oth.] - [arXiv] Point Cloud Super Resolution with Adversarial Residual Graph Networks. [
oth.] [tensorflow] - [arXiv] Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes. [
cls.rel.] - [arXiv] StarNet: Targeted Computation for Object Detection in Point Clouds. [tensorflow] [
det.] - [arXiv] Efficient Tracking Proposals using 2D-3D Siamese Networks on LIDAR. [
tra.] - [arXiv] SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud Processing. [tensorflow] [
cls.seg.] - [arXiv] Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. [
det.aut.] - [arXiv] PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation. [
cls.seg.] - [arXiv] PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing. [tensorflow] [
tra.oth.aut.] - [arXiv] PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points. [tensorflow] [
cls.seg.] - [arXiv] Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds. [
oth.] - [arXiv] 3D-Rotation-Equivariant Quaternion Neural Networks. [
cls.rec.] - [arXiv] Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules. [
cls.rel.seg.] - [arXiv] Geometric Feedback Network for Point Cloud Classification. [
cls.] - [arXiv] Relation Graph Network for 3D Object Detection in Point Clouds. [
det.] - [arXiv] Deformable Filter Convolution for Point Cloud Reasoning. [
seg.det.aut.] - [arXiv] PU-GCN: Point Cloud Upsampling via Graph Convolutional Network. [project] [
oth.] - [arXiv] Grid-GCN for Fast and Scalable Point Cloud Learning. [
seg.cls.] - [arXiv] PointPainting: Sequential Fusion for 3D Object Detection. [
seg.det.] - [arXiv] Transductive Zero-Shot Learning for 3D Point Cloud Classification. [
cls.] - [arXiv] Geometry Sharing Network for 3D Point Cloud Classification and Segmentation. [pytorch] [
cls.seg.] - [arvix] Deep Learning for 3D Point Clouds: A Survey. [code] [
cls.det.tra.seg.] - [arXiv] Spectral-GANs for High-Resolution 3D Point-cloud Generation. [
rec.oth.] - [arXiv] Point Attention Network for Semantic Segmentation of 3D Point Clouds. [
seg.] - [arXiv] PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation. [
oth.] - [arXiv] 3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models. [
cls.det.]
- [AAAI] Morphing and Sampling Network for Dense Point Cloud Completion. [pytorch] [
oth.] - [AAAI] TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. [code] [
det.aut.] - [AAAI] Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling. [
seg.cls.] - [AAAI] PRIN: Pointwise Rotation-Invariant Network. [
seg.cls.] - [AAAI] SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. [
oth] - [AAAI] JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds. [tensorflow][
seg.] - [AAAI] ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. [
det.] - [AAAI] Shape-Oriented Convolution Neural Network for Point Cloud Analysis. [
cls.] - [CVPR] RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [tensorflow] [
seg.] 🔥 - [CVPR] Learning multiview 3D point cloud registration. [code] [
reg.] - [CVPR] PF-Net: Point Fractal Network for 3D Point Cloud Completion. [pytorch] [
oth.] - [CVPR] ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes. [
det.] - [CVPR] Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. [pytorch] [
seg.] - [CVPR] AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss. [
seg.] - [CVPR] SA-SSD: Structure Aware Single-Stage 3D Object Detection from Point Cloud. [pytorch] [
det.] 🔥 - [CVPR] PointAugment: an Auto-Augmentation Framework for Point Cloud Classification. [code] [
classification.] - [CVPR] Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [tensorflow][
det.] 🔥 - [CVPR] Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds. [
seg.] - [CVPR] Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds. [pytorch][
oth.] - [CVPR] PointGMM: a Neural GMM Network for Point Clouds. [code][
cls.] - [CVPR] RPM-Net: Robust Point Matching using Learned Features. [code] [
seg.] - [CVPR] Unsupervised Learning of Intrinsic Structural Representation Points. [pytorch][
oth.] - [CVPR] PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. [pytorch] [
seg.] - [CVPR] 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. [
seg.] - [CVPR] DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes. [
det.] - [CVPR] OccuSeg: Occupancy-aware 3D Instance Segmentation. [
seg.] - [CVPR] MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps. [
oth.] - [CVPR] Learning to Segment 3D Point Clouds in 2D Image Space. [pytorch] [
seg] - [CVPR] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. [
cls] - [CVPR] PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. [
cls.] - [CVPR] Physically Realizable Adversarial Examples for LiDAR Object Detection. [
det.] - [CVPR] HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. [
det] - [CVPR] LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. [code][
det.] - [CVPR] PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. [
seg.] - [CVPR] DualSDF: Semantic Shape Manipulation using a Two-Level Representation. [code][
seg] - [CVPR] Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. [pytorch][
det.] - [CVPR] End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection. [code] [
det.] - [CVPR] Cascaded Refinement Network for Point Cloud Completion. [code][
completion] - [CVPR] MLCVNet: Multi-Level Context VoteNet for 3D Object Detection. [code][
det.] - [CVPR] Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions. [
oth.] - [CVPR] Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking. [
track.] - [CVPR] StructEdit: Learning Structural Shape Variations. [project] [
rec.] - [CVPR] Connect-and-Slice: an hybrid approach for reconstructing 3D objects. [
reconstruction.] - [CVPR] SGAS: Sequential Greedy Architecture Search. [pytorch] ['cls.']
- [CVPR oral] Deep Global Registration. ['reg.']
- [CVPR] 3DSSD: Point-based 3D Single Stage Object Detector. [
det] - [CVPR] Going Deeper with Point Networks. [pytorch]['cls.']
- [CVPR] Connect-and-Slice: an hybrid approach for reconstructing 3D objects. [
reconstruction] - [CVPR] Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. [
registration] - [CVPR] From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks. [tensorflow]['image-to-point cloud.']
- [CVPR] PointPainting: Sequential Fusion for 3D Object Detection. [
detection] - [CVPR] xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. [
Segmentation] - [CVPR] FroDO: From Detections to 3D Objects. [
detection] - [CVPR oral] OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression. [
Compression] - [CVPR] Train in Germany, Test in The USA: Making 3D Object Detectors Generalize. [code][
detection] - [CVPR oral] High-dimensional Convolutional Networks for Geometric Pattern Recognition. [code][
Recognition] - [CVPR oral] P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds. [pytorch][
Tracking] - [CVPR] Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. [
detection] - [CVPR] RevealNet: Seeing Behind Objects in RGB-D Scans. [
Completion] - [CVPR] A Hierarchical Graph Network for 3D Object Detection on Point Clouds. [
Detection] - [CVPR] Density Based Clustering for 3D Object Detection in Point Clouds. [
Detection] - [CVPR] Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. [
Detection] - [CVPR] Neural Implicit Embedding for Point Cloud Analysis. [
Analysis] - [CVPR] End-to-End 3D Point Cloud Instance Segmentation Without Detection. [
Segmentation] - [CVPR] Adaptive Hierarchical Down-Sampling for Point Cloud Classification. [
Classification] - [CVPR] Geometry and Learning Co-Supported Normal Estimation for Unstructured Point Cloud. [
Normal] - [CVPR] Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels. [
Segmentation] - [CVPR] SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel. [
Segmentation] - [CVPR] LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks. [
Attack] - [CVPR] SampleNet: Differentiable Point Cloud Sampling. [
Sampling] - [CVPR] Sequential 3D Human Pose and Shape Estimation From Point Clouds. [
Pose] - [CVPR] An Efficient PointLSTM for Point Clouds Based Gesture Recognition. [
Recognition] - [CVPR] Grid-GCN for Fast and Scalable Point Cloud Learning. [
other] - [CVPR] SpSequenceNet: Semantic Segmentation Network on 4D Point Clouds. [
Segmentation] - [CVPR] Point Cloud Completion by Skip-attention Network with Hierarchical Folding. [
Completion] - [CVPR] End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds. [
Description] - [CVPR] Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis. [
other] - [CVPR] On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks. [
other] - [CVPRW] AFDet: Anchor Free One Stage 3D Object Detection. [
Detection.] - [[ECCV]] EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. [code][
Detection] - [ECCV] 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. [code][
Detection] - [ECCV] GRNet: Gridding Residual Network for Dense Point Cloud Completion. [code][
Completion] - [ECCV] A Closer Look at Local Aggregation Operators in Point Cloud Analysis. [pytorch/tensorflow][
Analysis.] - [ECCV] Finding Your (3D) Center: 3D Object Detection Using a Learned Loss. [
Detection.] - [ECCV] H3DNet: 3D Object Detection Using Hybrid Geometric Primitives. [pytorch][
Detection.] - [ECCV] Quaternion Equivariant Capsule Networks for 3D Point Clouds. [
Classification] - [ECCV] Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation. [
Interpolation] - [ECCV] PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation. [
Flow] - [ECCV] H3DNet: 3D Object Detection Using Hybrid Geometric Primitives. [pytorch][
Detection.] - [ECCV] ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds. [
Fitting] - [ECCV] Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions. [code][
Learning] - [ECCV] DPDist : Comparing Point Clouds Using Deep Point Cloud Distance. [
Comparing] - [ECCV] SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds. [code][
Detection] - [ECCV] PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling. [
Upsampling] - [ECCV] AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds. [
Perturbations] - [ECCV] Learning Graph-Convolutional Representations for Point Cloud Denoising. [
Denoising] - [ECCV] Detail Preserved Point Cloud Completion via Separated Feature Aggregation. [tensorflow][
Completion] - [ECCV] Progressive Point Cloud Deconvolution Generation Network. [code][
Generation] - [ECCV] JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. [code][
Segmentation] - [ECCV] Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation. [pytorch][
Pose] - [ECCV] Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes. [
Correspondence] - [ECCV] Pillar-based Object Detection for Autonomous Driving. [tensorflow][
Detection] - [ECCV] DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. [pytorch][
Localization] - [ECCV] Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance. [
Meshing] - [ECCV] Discrete Point Flow Networks for Efficient Point Cloud Generation. [
Generation] - [ECCV] PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding. [
Unsupervised,Understanding] - [ECCV] Points2Surf: Learning Implicit Surfaces from Point Cloud Patches. [
Surfaces] - [ECCV] CAD-Deform: Deformable Fitting of CAD Models to 3D Scans. [
Fitting] - [ECCV] Weakly Supervised 3D Object Detection from Lidar Point Cloud. [
Detection] - [ECCV] Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds. [
Segmentation] - [ECCV] Virtual Multi-view Fusion for 3D Semantic Segmentation. [
Segmentation] - [ECCV] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. [
Segmentation] - [ECCV] Multimodal Shape Completion via Conditional Generative Adversarial Networks. [pytorch][
Completion] - [ECCV] PointMixup: Augmentation for Point Clouds. [code][
Classification] - [ECCV] SPOT: Selective Point Cloud Voting for Better Proposal in Point Cloud Object Detection. [
Detection] - [ECCV] Rotation-robust Intersection over Union for 3D Object Detection. [
3D IOU] - [ECCV] SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification. [
Classification, Completion] - [ECCV] Efficient Outdoor 3D Point Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts. [
Segmentation] - [ECCV] CN: Channel Normalization For Point Cloud Recognition. [
Recognition] - [ECCV] Weakly-supervised 3D Shape Completion in the Wild. [
Completion] - [ECCV] Deep FusionNet for Point Cloud Semantic Segmentation. [code][
Segmentation] - [IROS] Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation. [
Segmentation] - [IROS] Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation. [
Navigation] - [IROS] Factor Graph based 3D Multi-Object Tracking in Point Clouds. [
Tracking] - [IROS] Semantic Graph Based Place Recognition for 3D Point Clouds. [
Place Recognition] - [ACM MM] Weakly Supervised 3D Object Detection from Point Clouds. [code][
Detection] - [ACM MM] Differentiable Manifold Reconstruction for Point Cloud Denoising. [pytorch][
Denoising] - [ACM MM] Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene. [
Understanding] - [WACV] FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data. [
seg.aut.] - [WACV] Global Context Reasoning for Semantic Segmentation of 3D Point Clouds. [
seg.] - [WACV] PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation. [
oth.] - [BMVC] ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation. [
Segmentation] - [arXiv] Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes. [
oth.] - [arXiv] Multimodal Shape Completion via Conditional Generative Adversarial Networks. [
oth.] - [arXiv] MANet: Multimodal Attention Network based Point-View fusion for 3D Shape Recognition. [
cls.] - [arXiv] siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection. [
det.] - [arXiv] SalsaNext: Fast Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving. [code] [
seg.] - [arXiv] LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting. [
det.oth.] - [arXiv] Feature Fusion Network Based on Attention Mechanism for 3D Semantic Segmentation of Point Clouds. [
seg.] - [arXiv] Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. [
det.] - [arXiv] Self-supervised Point Set Local Descriptors for Point Cloud Registration. [
reg.] - [arXiv] How Powerful Are Randomly Initialized Pointcloud Set Functions?. [
cls.] - [arXiv] Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds. [
seg.] - [arXiv] PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization. [
oth.] - [arXiv] 3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction. [
det.] - [arXiv] 3D Point Cloud Processing and Learning for Autonomous Driving. [
oth.] - [arXiv] PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification. [
cls.] - [arXiv] PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions. [
oth.] - [arXiv] A Rotation-Invariant Framework for Deep Point Cloud Analysis. [
oth.] - [arXiv] Non-Local Part-Aware Point Cloud Denoising. [
oth.] - [arXiv] C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds. [
oth.] - [arXiv] DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. [
oth.] - [arXiv] Real-time 3D object proposal generation and classification under limited processing resources. [
det.] - [arXiv] Multi-view Semantic Learning Network for Point Cloud Based 3D Object Detection. [
seg.] - [arXiv] Sequential Forecasting of 100,000 Points. [
oth.] - [arXiv] Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways. [
seg.] - [arXiv] Self-Supervised Learning for Domain Adaptation on Point-Clouds. [
oth.] - [arXiv] A Benchmark for Point Clouds Registration Algorithms. [code][
seg.] - [arXiv] SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans. [
oth.] - [arXiv] ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds. [
oth.] - [arXiv] Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting. [pytorch][
oth.] - [arXiv] SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. [
cls.] - [arXiv] Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions. [pytorch][
oth.] - [arXiv] Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds. [
det.] - [arXiv] Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds. [
seg.] - [arXiv] Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene. [
seg.] - [arXiv] Quantifying Data Augmentation for LiDAR based 3D Object Detection. [code][
det.] - [arXiv] Generative PointNet: Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification. [
oth.] - [arXiv] Deformation-Aware 3D Model Embedding and Retrieval. [
oth.] - [arXiv] Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation. [
oth.] - [arXiv] SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds. [code][
det.] - [arXiv] SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. [code][
seg.] - [arXiv] Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds. [
seg.] - [arXiv] MNEW: Multi-domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation. [
seg.] - [arXiv] LightConvPoint: convolution for points. [
cls.] - [arXiv] 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. [
det.] - [arXiv] Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review. [
review.] - [arXiv] Simulation-based Lidar Super-resolution for Ground Vehicles. [tensorflow][
oth.] - [arXiv] Deep Manifold Prior. [
oth.] - [arXiv] Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network. [
cls.] - [arXiv] Semantic Correspondence via 2D-3D-2D Cycle. [code][
oth.] - [arXiv] DAPnet: A double self-attention convolutional network for segmentation of point clouds. [code] [
seg.] - [arXiv] DPDist : Comparing Point Clouds Using Deep Point Cloud Distance. [
seg.] - [arXiv] 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. [
det.] - [arXiv] Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes. [
seg.] - [arXiv] CoReNet: Coherent 3D scene reconstruction from a single RGB image. [
reconstruction.] - [arXiv] MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling. [
sampling.] - [arXiv] PointTriNet: Learned Triangulation of 3D Point Sets. [
Triangulation.] - [arXiv] Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds. [
detection.] - [arXiv] Point Cloud Completion by Skip-attention Network with Hierarchical Folding. [
Completion.] - [arXiv] Dense-Resolution Network for Point Cloud Classification and Segmentation. [
segmentation.] - [arXiv] Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data. [
segmentation.] - [arXiv] Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review. [
Review.] - [arXiv] hapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds. [
Generation.] - [arXiv] Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. [
Detection.] - [arXiv] PAI-Conv: Permutable Anisotropic Convolutional Networks for Learning on Point Clouds. [
Classification.] - [arXiv] ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds. [
Generation.] - [arXiv] SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. [
Detection.] - [arXiv] Are We Hungry for 3D LiDAR Data for Semantic Segmentation? [
Segmentation.] - [arXiv] GRNet: Gridding Residual Network for Dense Point Cloud Completion. [
Completion.] - [arXiv] Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration. [
Registration.] - [arXiv] Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion. [
Completion.] - [arXiv] 3D Point Cloud Feature Explanations Using Gradient-Based Methods. [
other.] - [arXiv] Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection. [
Detection.] - [arXiv] H3DNet: 3D Object Detection Using Hybrid Geometric Primitives. [pytorch][
Detection.] - [arXiv] Generative Sparse Detection Networks for 3D Single-shot Object Detection. [
Detection.] - [arXiv] Center-based 3D Object Detection and Tracking. [pytorch][
Detection.] - [arXiv] 1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation. [
Detection.] - [arXiv] 1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking. [
Detection.] - [arXiv] PIE-NET: Parametric Inference of Point Cloud Edges. [
Edge Detection.] - [arXiv] Point Set Voting for Partial Point Cloud Analysis. [
Segmentation,Classification,Completion.] - [arXiv] Geometric Attention for Prediction of Differential Properties in 3D Point Clouds. [
Feature Line.] - [arXiv] Local Grid Rendering Networks for 3D Object Detection in Point Clouds. [
Detection.] - [arXiv] Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. [
Segmentation.] - [arXiv] Accelerating 3D Deep Learning with PyTorch3D. [
PyTorch3D.] - [arXiv] Part-Aware Data Augmentation for 3D Object Detection in Point Cloud. [
Detection.] - [arXiv] Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation. [code][
Segmentation.] - [arXiv] CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations. [
Representation.] - [arXiv] Global Context Aware Convolutions for 3D Point Cloud Understanding. [
Understanding.] - [arXiv] LPMNet: Latent Part Modification and Generation for 3D Point Clouds. [
Generation.] - [arXiv] VPC-Net: Completion of 3D Vehicles from MLS Point Clouds. [
Completion.] - [arXiv] Projected-point-based Segmentation: A New Paradigm for LiDAR Point Cloud Segmentation. [
Segmentation.] - [arXiv] PAM:Point-wise Attention Module for 6D Object Pose Estimation. [
Pose.] - [arXiv] Self-Sampling for Neural Point Cloud Consolidation. [
Consolidation.] - [arXiv] Deterministic PointNetLK for Generalized Registration. [
Registration.] - [ICML] PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing. [
Classification.] - [ICRA] DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans. [
seg.] - [ICRA] PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation. [
completion.] - [ICRA] Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. [
cls.] - [ICRA] Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds. [
det.] - [TPAMI] Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. [
cls.] - [ICLR] Unpaired Point Cloud Completion on Real Scans using Adversarial Training.[tensorflow] [
com.] - [ACIIDS] Semi-supervised Representation Learning for 3D Point Clouds. [
oth.] - [CG] ConvPoint: Continuous convolutions for point cloud processing. [
oth.] - [ISPRS] Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global. [
oth.] - [GMP] LRC-Net: Learning Discriminative Features on Point Clouds by EncodingLocal Region Contexts. [
cls.] - [SPM] Deep Feature-preserving Normal Estimation for Point Cloud Filtering. [
normal.] - [Master Thesis] Neighborhood Pooling in Graph Neural Networks for 3D and 4D Semantic Segmentation. ['seg.']
- [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] [ApolloScape] The datasets are provided by Baidu Inc. [
tra.seg.det.] - [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, (c) 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]
- [SynthCity] SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Nine categories. [
seg.aut.] - [Lyft Level 5] Include high quality, human-labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map. [
det.seg.aut.] - [SemanticKITTI] Sequential Semantic Segmentation, 28 classes, for autonomous driving. All sequences of KITTI odometry labeled. [ICCV 2019 paper] [
seg.oth.aut.] - [NPM3D] The Paris-Lille-3D has been produced by a Mobile Laser System (MLS) in two different cities in France (Paris and Lille). [
seg.] - [The Waymo Open Dataset] The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions. [
det.] - [A*3D: An Autonomous Driving Dataset in Challeging Environments] A*3D: An Autonomous Driving Dataset in Challeging Environments. [
det.] - [PointDA-10 Dataset] Domain Adaptation for point clouds.
- [Oxford Robotcar] The dataset captures many different combinations of weather, traffic and pedestrians. [
cls.det.rec.] - [WHU-TLS BENCHMARK] WHU-TLS benchmark dataset. [
reg.] - [DALES] DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation. [
seg.]