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.]

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