awesome-point-cloud-analysis

for anyone who wants to do research about 3D point cloud.
I will try to update this list everyday!!!

- Recent papers (from 2017)

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

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.] 🔥 ⭐
[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.]
[ICCV] Local-to-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors. [reg.]
[ICCV] Point Set Registration with Global-Local Correspondence and Transformation Estimation. [reg.]
[AAAI] Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization. [reg.]

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.]
[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.]

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. [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.]

2020

[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]
[CVPR] 3DRegNet: A Deep Neural Network for 3D Point Registration. [reg.]
[CVPR] Global Optimality for Point Set Registration Using Semidefinite Programming. [reg.]
[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]
[ECCV] SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. [code][seg.]
[ECCV] InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling. [Detection]
[[ECCV] Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [reg.]
[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. [code][cls.]
[arXiv] PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions. [code][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. [code][seg.]
[arXiv] Self-Supervised Learning for Domain Adaptation on Point-Clouds.[code] [oth.]
[arXiv] A Benchmark for Point Clouds Registration Algorithms. [code][reg.]
[arXiv] SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans.[code] [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. [pytorch] [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] 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. [code][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.[code] [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.]
[arXiv] A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds.[Detection]
[arXiv] A Self Contour-based Rotation and Translation-Invariant Transformation for Point Clouds Recognition.[code] [Recognition ]
[arXiv] Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation. [Segmentation]
[arXiv] Deep Learning for 3D Point Cloud Understanding: A Survey. [code] [Survey]
[arXiv] Improving Point Cloud Semantic Segmentation by Learning 3D Object Proposal Generation. [Segmentation]
[arXiv] MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. [Detection]
[arXiv] Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector. [Registration]
[arXiv] Graph-based methods for analyzing orchard tree structure using noisy point cloud data. [ ]
[arXiv] Pre-Training by Completing Point Clouds[torch]. [Completion]
[arXiv] Discriminative and Generative Models for Anatomical Shape Analysis on Point Clouds with Deep Neural Networks . [Analysis ]
[arXiv] Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds. [Grasping]
[arXiv] On the Universality of Rotation Equivariant Point Cloud Networks.[Analysis]
[arXiv] Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels. [Detection ]
[arXiv] Torch-Points3D: A Modular Multi-Task Framework for Reproducible Deep Learning on 3D Point Clouds.[torch][Framework]
[arXiv] Refinement of Predicted Missing Parts Enhance Point Cloud Completion.[torch][Completion]
[arXiv] A Self-supervised Cascaded Refinement Network for Point Cloud Completion. [ Completion]
[arXiv] Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration. [ Registration]
[arXiv] Learning to Reconstruct and Segment 3D Objects. [ Segmentation；Reconstruction]
[arXiv] Generating Large Convex Polytopes Directly on Point Clouds. [Segmentation]
[arxiv] Human Segmentation with Dynamic LiDAR Data. [ Segmentation]
[arXiv] Representing Point Clouds with Generative Conditional Invertible Flow Networks. [Representation]
[arXiv] 3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence. [Correspondence]
[arXiv] MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving.[code][Detection ]
[arXiv] 3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds. [Registration ]
[arXiv] Multi-View Adaptive Fusion Network for 3D Object Detection. [Detection]
[arXiv] MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis. [code][Analysis]
[arXiv] Point Transformer. [Analysis]
[arXiv] Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. [Detection]
[arXiv] BiPointNet: Binary Neural Network for Point Clouds. [Analysis]
[arXiv] Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis. [Rotation]
[arXiv] Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes. [code][Detection]
[arXiv] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration. [code][Registration]
[arXiv] Multi-Features Guidance Network for partial-to-partial point cloud registration. [registration]
[arXiv] MoNet: Motion-based Point Cloud Prediction Network. [Motion]
[arXiv] Recalibration of Neural Networks for Point Cloud Analysis. [Recalibration]
[arXiv] Deep Magnification-Arbitrary Upsampling over 3D Point Clouds. [Upsampling]
[arXiv] Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds.[Detection]
[arXiv] Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds.[ Segmentation]
[arXiv] DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution.[code][Segmentation ]
[arXiv] PREDATOR: Registration of 3D Point Clouds with Low Overlap.[Registration ]
[arXiv] Learning-based lossless compression of 3D point cloud geometry.[Compression]
[arXiv] Occlusion Guided Scene Flow Estimation on 3D Point Clouds.[Flow]
[arXiv] Learning geometry-image representation for 3D point cloud generation.[Generation]
[arXiv] Deeper or Wider Networks of Point Clouds with Self-attention?[Networks]
[arXiv] DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes[Features]
[arXiv] AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation.[Segmentation]
[arXiv] vLPD-Net: A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition.[Registration]
[arXiv] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection.[code][Detection ]
[arXiv] SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization.[Upsampling]
[arXiv] ParaNet: Deep Regular Representation for 3D Point Clouds.[Representation]
[IROS] PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation. [Segmentation ]
[IROS] RegionNet: Region-feature-enhanced 3D Scene Understanding Network with Dual Spatial-aware Discriminative Loss. [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]
[IROS] PillarFlowNet: A Real-time Deep Multitask Network for LiDAR-based 3D Object Detection and Scene Flow Estimation. [Detection, Flow]
[IROS] End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences.[Registration]
[3DV] SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection. [Detection]
[3DV] Scene Flow from Point Clouds with or without Learning. [Flow ]
[3DV] Registration Loss Learning for Deep Probabilistic Point Set Registration. [pytorch][ Registration]
[3DV] MaskNet: A Fully-Convolutional Network to Estimate Inlier Points. [pytorch][ Registration]
[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]
[NIPS] Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud. [Representation]
[NIPS] MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models. [Compression]
[NIPS] Group Contextual Encoding for 3D Point Clouds. [Detection]
[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.']

2021

[arXiv] 3D Object Detection with Pointformer. [Detection.]
[arXiv] DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. [Segmentation.]
[arXiv] SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. [Segmentation.]
[arXiv] PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection. [Detection.]
[arXiv] LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds. [Segmentation.]
[arXiv] 3D Object Classification on Partial Point Clouds: A Practical Perspective. [Classification.]
[arXiv] PointINet: Point Cloud Frame Interpolation Network. [Detection.]
[arXiv] PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection. [Detection.]
[arXiv] PCT: Point Cloud Transformer. [Transformer.]
[arXiv] SceneFormer: Indoor Scene Generation with Transformers. [Transformer.]
[arXiv] Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. [Understanding.]
[arXiv] Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition. [tensorflow] [Place Recognition.]
[arXiv] Self-Supervised Pretraining of 3D Features on any Point-Cloud.[pytorch] [Self-Supervised.]
[arXiv] Self-Attention Based Context-Aware 3D Object Detection. [pytorch] [Detection.]
[arXiv] PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [pytorch] [Detection.]
[arXiv] DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds. [Detection.]
[arXiv] Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds. [Learning.]
[arXiv] Point-set Distances for Learning Representations of 3D Point Clouds. [Distances.]
[arXiv] Weakly Supervised Learning of Rigid 3D Scene Flow. [pytorch] [Scene Flow.]
[arXiv] Attention Models for Point Clouds in Deep Learning: A Survey. [Survey.]
[arXiv] Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction. [Upsampling.____Reconstruction.]
[ICLR] PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences. [Sequences]
[ICLR] Learning to Generate 3D Shapes with Generative Cellular Automata. [Generation]
[TOG] PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds [Edge Detection.]
[CoRL] Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks. [code] [Prediction.]
[CVPR] Diffusion Probabilistic Models for 3D Point Cloud Generation. [code][Generation]
[CVPR] PREDATOR: Registration of 3D Point Clouds with Low Overlap. [pytorch][Registration]
[CVPR] Style-based Point Generator with Adversarial Rendering for Point Cloud Completion. [Completion]
[CVPR] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration. [pytorch][Registration]
[CVPR oral] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization. [code][Synchronization]
[CVPR] Center-based 3D Object Detection and Tracking. [pytorch][Detection]
[CVPR] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection. [pytorch][Detection]
[CVPR] PointGuard: Provably Robust 3D Point Cloud Classification. [Classification]
[CVPR] TPCN: Temporal Point Cloud Networks for Motion Forecasting. [Motion]
[CVPR] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. [code][Segmentation]
[CVPR] Robust Point Cloud Registration Framework Based on Deep Graph Matching. [code][Registration]
[CVPR] ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection. [code][Detection]
[CVPR] PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency. [Registration]
[CVPR] Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis. [Scene analysis]
[CVPR] How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines. [code][Scene recover]
[CVPR] Point2Skeleton: Learning Skeletal Representations from Point Clouds. [code][Skeleton]
[CVPR] Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding. [Completion]
[CVPR] Monte Carlo Scene Search for 3D Scene Understanding. [Understanding]
[CVPR] AF2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network. [Segmentation]
[CVPR] Equivariant Point Network for 3D Point Cloud Analysis. [Analysis]
[CVPR] PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds. [code][Convolution]
[CVPR] Point Cloud Instance Segmentation using Probabilistic Embeddings. [Segmentation]
[CVPR] Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation. [Segmentation]
[CVPR] ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning. [Registration]
[CVPR] LiDAR R-CNN: An Efficient and Universal 3D Object Detector. [code][Detection]
[CVPR] HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection. [Detection]
[CVPR] FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds. [Flow]
[CVPR] DeepI2P: Image-to-Point Cloud Registration via Deep Classification. [code][Registration]
[CVPR] Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds. [pytorch][Detection]
[CVPR] Unsupervised 3D Shape Completion through GAN Inversion. [pytorch][Completion]
[CVPR] SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. [code][Detection]
[CVPR oral] Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos. [pytorch][Transformer]
[CVPR] VoxelContext-Net: An Octree based Framework for Point Cloud Compression. [Compression]
[CVPR] HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding. [Flow]
[CVPR] Offboard 3D Object Detection from Point Cloud Sequences. [Detection]
[CVPR] CorrNet3D: Unsupervised End-to-End Learning of Dense Correspondence for 3D Point Clouds. [code][Correspondence]
[CVPR] Self-Point-Flow: Self-Supervised Scene Flow Estimation From Point Clouds With Optimal Transport and Random Walk. [Flow]
[CVPR] SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud Based Place Recognition. [code][Recognition]
[CVPR] Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion. [Segmentation]
[CVPR] Equivariant Point Network for 3D Point Cloud Analysis. [code][Analysis]
[CVPR] Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. [Segmentation]
[CVPR] Regularization Strategy for Point Cloud via Rigidly Mixed Sample. [Regularization]
[CVPR] StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks. [Matching]
[CVPR] Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing. [Processing]
[CVPR] PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks. [code][Upsampling]
[CVPR] SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation.[code] [Segmentation]
[CVPR] UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering. [Registration]
[CVPR] Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models. [Self-Supervised]
[CVPR] Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction From Raw Point Clouds. [Self-Similarities]
[CVPR] Learning Progressive Point Embeddings for 3D Point Cloud Generation. [Generation]
[CVPR] DyCo3D: Robust Instance Segmentation of 3D Point Clouds Through Dynamic Convolution. [code][Segmentation]
[CVPR] TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations. [Autoencoder]
[CVPR] PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization. [code][Odometry]
[CVPR] PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds. [Flow]
[CVPR] CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation. [Segmentation]
[CVPR] Point Cloud Upsampling via Disentangled Refinement. [code][Upsampling]
[CVPR] PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths. [code][Completion]
[CVPR] View-Guided Point Cloud Completion. [Completion]
[CVPR] Few-shot 3D Point Cloud Semantic Segmentation. [code][Segmentation]
[CVPR] 3D Spatial Recognition without Spatially Labeled 3D. [code][Detection]
[CVPR] Skeleton Merger: an Unsupervised Aligned Keypoint Detector. [code][Skeleton]
[CVPR] Variational Relational Point Completion Network. [code][Completion]
[CVPR] DeCo: Denoise and Contrast for Category Agnostic Shape Completion. [code][Completion]
[CVPR] PointNetLK Revisited. [code][Registration]
[CVPR] PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features. [Detection]
[ICCV] DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation. [Segmentation]
[ICCV] Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching. [code][Generation]
[ICCV] Score-Based Point Cloud Denoising. [Denoising]
[ICCV] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration. [code][Registration]
[ICCV oral] (Just) A Spoonful of Refinements Helps the Registration Error Go Down. [Registration]
[ICCV] ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. [Segmentation]
[ICCV] Learning with Noisy Labels for Robust Point Cloud Segmentation. [code][Segmentation]
[ICCV] Adaptive Graph Convolution for Point Cloud Analysis. [pytorch][Analysis]
[ICCV] ME-PCN: Point Completion Conditioned on Mask Emptiness. [Completion]
[ICCV] RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection. [Detection]
[ICCV] Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. [code][Segmentation]
[ICCV] Hierarchical Aggregation for 3D Instance Segmentation. [code][Segmentation]
[ICCV] Learning Inner-Group Relations on Point Clouds. [Learning]
[ICCV] A Robust Loss for Point Cloud Registration. [Registration]
[ICCV] Improving 3D Object Detection with Channel-wise Transformer. [Detection]
[ICCV] patio-temporal Self-Supervised Representation Learning for 3D Point Clouds. [Learning]
[ICCV] Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. [Detection]
[ICCV] Voxel Transformer for 3D Object Detection. [Detection]
[ICCV] Deep Hough Voting for Robust Global Registration. [Registration]
[ICCV] SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer. [code][Completion]
[ICCV] Point Transformer. [Transformer]
[ICCV] Group-Free 3D Object Detection via Transformers. [code][Detection]
[ICCV] Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration. [Registration]
[ICCV oral] An End-to-End Transformer Model for 3D Object Detection. [code][Detection]
[ICCV] Self-Supervised Pretraining of 3D Features on any Point-Cloud. [code][Detection]
[ICCV] SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. [Detection]
[ICCV] VENet: Voting Enhancement Network for 3D Object Detection. [Detection]
[ICCV] MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking.[code] [Tracking]
[ICCV] Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection. [Detection]
[ICCV] TempNet: Online Semantic Segmentation on Large-scale Point Cloud Series. [Segmentation]
[ICCV] Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation. [Segmentation]
[ICCV] Guided Point Contrastive Learning for Semi-Supervised Point Cloud Semantic Segmentation. [Segmentation]
[ICCV] Unsupervised Point Cloud Pre-training via Occlusion Completion.[code] [training]
[ICCV] Superpoint Network for Point Cloud Oversegmentation.[code] [Segmentation]
[ICCV] SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix. [Representations]
[ICCV] Point Cloud Augmentation with Weighted Local Transformations.[code] [Augmentation]
[ICCV] A Closer Look at Rotation-invariant Deep Point Cloud Analysis. [Analysis]
[ICCV] 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds. [Visual Grounding]
[ICCV] Pyramid Point Cloud Transformer for Large-Scale Place Recognition.[code] [Recognition]
[ICCV] LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration.[code] [Registration]
[ICCV] Differentiable Convolution Search for Point Cloud Processing. [Processing]
[ICCV] Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather.[code] [Detection]
[ICCV] RFNet: Recurrent Forward Network for Dense Point Cloud Completion. [Completion]
[ICCV] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers.[code] [Completion]
[ICCV] PICCOLO: Point Cloud-Centric Omnidirectional Localization. [Localization]
[ICCV] PU-EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling. [Upsampling]
[ICCV] A Backdoor Attack Against 3D Point Cloud Classifiers.[code] [Classification]
[ICCV] Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility.[code] [Reconstruction]
[ICCV] 3D Shape Generation and Completion through Point-Voxel Diffusion.[code] [Completion]
[ICCV] Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching.[code] [Generation]
[ICCV] OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration.[code] [Registration]
[ICCV] Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks. [Transformer]
[ICCV] Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds.[code] [Tracking]
[ICCV] DeepPRO: Deep Partial Point Cloud Registration of Objects. [Registration]
[ICCV] Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis.[code] [Curves]
[ICCV] Feature Interactive Representation for Point Cloud Registration.[code] [Tracking]
[ICCV] CAPTRA: CAtegory-Level Pose Tracking for Rigid and Articulated Objects From Point Clouds.[code] [Pose Tracking]
[ICCV] DWKS: A Local Descriptor of Deformations Between Meshes and Point Clouds.[code] [Descriptor]
[ICCV] Provably Approximated Point Cloud Registration. [Registration]
[ICCV] Free-Form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud.[code] [Grounding]
[ICCV] Deep Implicit Surface Point Prediction Networks.[code] [Tracking]
[ICCV] Distinctiveness oriented Positional Equilibrium for Point Cloud Registration. [Registration]
[ICCV] PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds. [Registration]
[ICCV] CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds.[code] [Fitting]
[ICCV] PointBA: Towards Backdoor Attacks in 3D Point Cloud. [Attacks]
[ICCV] Unsupervised Point Cloud Object Co-Segmentation by Co-Contrastive Learning and Mutual Attention Sampling.[code][Segmentation]
[ICCV] Robustness Certification for Point Cloud Models.[Certification]
[ICCV] RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation.[Segmentation]
[ICCV] Minimal Adversarial Examples for Deep Learning on 3D Point Clouds.[Adversarial]
[ICCV] Shape Self-Correction for Unsupervised Point Cloud Understanding.[Understanding]
[ICCV] AdaFit: Rethinking Learning-Based Normal Estimation on Point Clouds. [code] [Normal Estimation]
[ICCV] Towards Efficient Graph Convolutional Networks for Point Cloud Handling.[Graph]
[ICCV] InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds Through Instance Multi-Level Contextual Referring. [code] [Visual Grounding]
[ICCV] Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds. [code] [Domain Adaptation]
[ICCV] Progressive Seed Generation Auto-Encoder for Unsupervised Point Cloud Learning.[Encoder]
[ICCV] You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking. [code] [Detection]
[CVPR] RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation [reg.]
[TPAMI] Acceleration of Non-Rigid Point Set Registration With Downsampling and Gaussian Process Regression. [reg.]
[TPAMI] Point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization. [reg.]
[TPAMI] Topology-Aware Non-Rigid Point Cloud Registration. [reg.]
[NeurIPS] CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration. [reg.]
[NeurIPS] Accurate Point Cloud Registration with Robust Optimal Transport. [reg.]

2022

[arXiv] MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds. [Segmentation.]
[arXiv] TPC: Transformation-Specific Smoothing for Point Cloud Models. [Classification.]
[arXiv] Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions. [code] [Classification.]
[arXiv] Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification. [code] [Classification.]
[arXiv] Neighborhood-aware Geometric Encoding Network for Point Cloud Registration. [code] [Registration.]
[arXiv] Benchmarking and Analyzing Point Cloud Classification under Corruptions. [code] [Classification.]
[arXiv] Geometric Transformer for Fast and Robust Point Cloud Registration. [code] [Registration.]
[arXiv] OctAttention: Octree-based Large-scale Contexts Model for Point Cloud Compression. [code] [Compression.]
[WACV] M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers. [code] [Detection.]
[ICLR] Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework. [code] [Classification]
[ICLR] Deep Point Cloud Reconstruction. [Reconstruction.]
[AAAI] SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. [code] [Detection]
[CVPR] PointCLIP: Point Cloud Understanding by CLIP. [code] [Understanding]
[CVPR] Point-NeRF: Point-based Neural Radiance Fields. [code] [Reconstruction]
[CVPR] SoftGroup for 3D Instance Segmentation on Point Clouds. [code] [Segmentation]
[CVPR] Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds. [code] [Tracking]
[CVPR] Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders. [Fitting]
[CVPR] CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding. [code] [Understanding]
[CVPR] ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation. [code] [Rotation]
[CVPR] Shape-invariant 3D Adversarial Point Clouds. [code] [Adversarial]
[CVPR] Back To Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement. [code] [Detection]
[CVPR] Contrastive Boundary Learning for Point Cloud Segmentation. [code] [Segmentation]
[CVPR] Point Density-Aware Voxels for LiDAR 3D Object Detection. [code] [Detection]
[CVPR] AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation. [Segmentation]
[CVPR] DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. [code] [Detection]
[CVPR] Scribble-Supervised LiDAR Semantic Segmentation. [code] [Segmentation]
[CVPR] Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling. [code] [BERT]
[CVPR] Not All Points Are Equal: Learning Highly Efficient Point-based Detectorsfor 3D LiDAR Point Clouds. [code] [Detection]
[CVPR] Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. [code] [Detection]
[CVPR] TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. [code] [Detection]
[CVPR] IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment. [code] [Interpolation]
[CVPR] AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception. [code] [Symmetry]
[CVPR] WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation. [code] [Generation]
[CVPR] Point2Seq: Detecting 3D Objects as Sequences. [code] [Detection]
[CVPR] REGTR: End-to-end Point Cloud Correspondences with Transformers. [code] [Registration]
[CVPR] Stratified Transformer for 3D Point Cloud Segmentation. [code] [Segmentation]
[CVPR] Equivariant Point Cloud Analysis via Learning Orientations for Message Passing. [code] [Classification]
[CVPR] SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration. [code] [Registration]
[CVPR] LiDAR Snowfall Simulation for Robust 3D Object Detection. [code] [Detection]
[CVPR] Learning a Structured Latent Space for Unsupervised Point Cloud Completion. [Completion]
[CVPR] Text2Pos: Text-to-Point-Cloud Cross-Modal Localization. [code] [Localization]
[CVPR] 3DAC: Learning Attribute Compression for Point Clouds. [Compression]
[CVPR] No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces. [code] [Flow]
[CVPR] CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. [Detection]
[CVPR] Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds. [code] [Flow]
[CVPR] Multi-View Transformer for 3D Visual Grounding. [code] [Grounding]
[CVPR] Learning to Detect Mobile Objects from LiDAR Scans Without Labels. [code] [Detection]
[CVPR] RBGNet: Ray-based Grouping for 3D Object Detection. [code] [Detection]
[CVPR] Bridged Transformer for Vision and Point Cloud 3D Object Detection. [Detection]
[CVPR oral] 3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection. [code] [Visual Grounding]
[CVPR] Geometric Transformer for Fast and Robust Point Cloud Registration. [code] [Registration]
[CVPR] PTTR: Relational 3D Point Cloud Object Tracking with Transformer. [code] [Tracking]
[CVPR] Rotationally Equivariant 3D Object Detection. [code] [Detection]
[CVPR] MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation. [code] [Segmentation]
[CVPR] Density-preserving Deep Point Cloud Compression. [code] [Compression]
[CVPR] 3DeformRS: Certifying Spatial Deformations on Point Clouds. [code] [Deformations]
[CVPR oral] Surface Representation for Point Clouds. [code] [Representation]
[CVPR] SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation. [code] [Segmentation]
[CVPR] RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds. [code] [Flow]
[CVPR] 3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds. [Grounding]
[CVPR] Multi-instance Point Cloud Registration by Efficient Correspondence Clustering. [code] [Registration]
[CVPR] Lepard: Learning partial point cloud matching in rigid and deformable scenes. [code] [Matching]
[CVPR] Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients. [code] [Attack]
[CVPR] Neural Points: Point Cloud Representation With Neural Fields for Arbitrary Upsampling. [code] [Upsampling]
[CVPR] Point Cloud Pre-training with Natural 3D Structures. [code] [Pre-training]
[CVPR] A Unified Query-based Paradigm for Point Cloud Understanding. [Understanding]
[CVPR] Surface Reconstruction From Point Clouds by Learning Predictive Context Priors. [code] [Reconstruction]
[CVPR] RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior. [Flow]
[CVPR] Deterministic Point Cloud Registration via Novel Transformation Decomposition. [Registration]
[CVPR] 3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection. [code] [Detection]
[CVPRW] PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences [oth.]
[CVPR] An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation. [code] [Segmentation]
[CVPR] Why Discard if You Can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud Analysis. [Analysis]
[CVPR] Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation. [code] [Segmentation]
[CVPR] Finding Good Configurations of Planar Primitives in Unorganized Point Clouds. [Primitives]
[CVPR] No-Reference Point Cloud Quality Assessment via Domain Adaptation. [code] [Assessment]
[CVPR] Learning Local Displacements for Point Cloud Completion. [Completion]
[CVPR] Point Cloud Color Constancy. [code] [Color]
[CVPR] Multimodal Colored Point Cloud to Image Alignment. [Alignment]
[CVPR] Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation. [code] [Upsampling]
[CVPR] LiDARCap: Long-Range Marker-Less 3D Human Motion Capture With LiDAR Point Clouds. [Motion]
[CVPR] Domain Adaptation on Point Clouds via Geometry-Aware Implicits. [code] [Adaptation]
[CVPR] Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation. [Segmentation]
[CVPR] LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints. [Completion]
[CVPR] SS3D: Sparsely-Supervised 3D Object Detection from Point Cloud. [Detection]
[CVPR] Upright-Net: Learning Upright Orientation for 3D Point Cloud. [Orientation]
[CVPR] Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors. [code] [Reconstruction]
[CVPR] HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization. [Segmentation]
[CVPR] Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation with Reliable Voted Pseudo Labels. [Adaptation]
[CVPR] Boosting 3D Object Detection by Simulating Multimodality on Point Clouds. [Detection]
[CVPR] DiGS: Divergence Guided Shape Implicit Neural Representation for Unoriented Point Clouds. [code] [Neural Representations]
[ECCV] Open-world semantic segmentation for Lidar Point Clouds. [code] [Segmentation]
[ECCV] 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds. [code] [Segmentation]
[ECCV] CPO: Change Robust Panorama to Point Cloud Localization. [Localization]
[ECCV] Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. [code] [Segmentation]
[ECCV] Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds. [code] [Sampling]
[ECCV] Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation. [code] [Scene Flow]
[ECCV] Masked Discrimination for Self-Supervised Learning on Point Clouds. [code] [Self-Supervised Learning]
[ECCV] 3D Siamese Transformer Network for Single Object Tracking on Point Clouds. [code] [Tracking]
[ECCV] Salient Object Detection for Point Clouds. [code] [Detection]
[ECCV] Label-Guided Auxiliary Training Improves 3D Object Detector. [code] [Detection]
[ECCV] Dynamic 3D Scene Analysis by Point Cloud Accumulation. [code] [Scene]
[ECCV] MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud. [code] [Fit]
[ECCV] Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding. [Video Understanding]
[ECCV] SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud. [code] [Segmentation]
[ECCV] SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty. [De-snowing]
[ECCV] Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. [code] [Detection]
[ECCV] PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees. [code] [Encoder]
[ECCV] Learning to Generate Realistic LiDAR Point Clouds. [code] [LiDAR]
[ECCV] FBNet: Feedback Network for Point Cloud Completion. [code] [Completion]
[ECCV] RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds. [code] [Flow]
[ECCV] LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds. [Segmentation]
[ECCV] FH-Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds. [code] [Flow]
[ECCV] Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World. [code] [Registration]
[ECCV] CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement. [code] [Registration]
[ECCV] Optimization over Disentangled Encoding: Unsupervised Cross-Domain Point Cloud Completion via Occlusion Factor Manipulation. [code] [Completion]
[ECCV] Unsupervised Learning of 3D Semantic Keypoints with Mutual Reconstruction. [code] [Reconstruction]
[ECCV] Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach. [Simplification]
[ECCV] Lidar Point Cloud Guided Monocular 3D Object Detection. [code] [Detection]

2023

[CVPR] PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction. [code] [Reconstruction]
[CVPR] Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions. [code] [Upsampling]
[CVPR] Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection. [code] [Detection]
[CVPR] Curricular Object Manipulation in LiDAR-based Object Detection. [code] [Detection]
[CVPR] Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration. [code] [Registration]
[CVPR] BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration. [code] [Registration]
[CVPR] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders. [code] [Representations]
[CVPR] Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis. [code] [Representations]
[CVPR] Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving. [code] [Benchmark]
[CVPR] AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers. [code] [Detection]
[CVPR] CXTrack: Improving 3D Point Cloud Tracking With Contextual Information. [Tracking]
[CVPR] NeuralPCI: Spatio-Temporal Neural Field for 3D Point Cloud Multi-Frame Non-Linear Interpolation. [code] [Interpolation]
[CVPR] Spatiotemporal Self-Supervised Learning for Point Clouds in the Wild. [code] [Understanding]
[CVPR] Frequency-Modulated Point Cloud Rendering With Easy Editing. [code] [Nerf]
[CVPR] SE-ORNet: Self-Ensembling Orientation-Aware Network for Unsupervised Point Cloud Shape Correspondence. [Correspondence]
[CVPR] Hyperspherical Embedding for Point Cloud Completion. [Completion]
[CVPR] MSF: Motion-Guided Sequential Fusion for Efficient 3D Object Detection From Point Cloud Sequences. [code] [Detection]
[ICCV] 3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack. [Attack]
[ICCV] 3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking. [code] [Tracking]
[ICCV] U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds. [code] [Retrieval]
[ICCV] 2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds. [code] [Registration]
[ICCV] Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation. [code] [Pose]
[ICCV] 3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers. [code] [Detection]
[ICCV] GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds. [code] [Detection]
[ICCV] P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds. [code] [Completion]
[ICCV] SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data. [code] [Keypoints]

- 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] [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.]

Name		Name	Last commit message	Last commit date
Latest commit History 421 Commits
README.md		README.md

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

awesome-point-cloud-analysis

- Recent papers (from 2017)

Table of Contents

Keywords

2017

2018

2019

2020

2021

2022

2023

About

Releases

Packages

NUAAXQ/awesome-point-cloud-analysis-2023

Folders and files

Latest commit

History

Repository files navigation

awesome-point-cloud-analysis

- Recent papers (from 2017)

Table of Contents

Keywords

2017

2018

2019

2020

2021

2022

2023

About

Resources

Stars

Watchers

Forks

Releases

Packages 0

Packages