Point Cloud 2019

时间 2020-07-14 标签 point cloud

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.]
[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.]
[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] RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [tensorflow] [seg.]
[arXiv] Geometric Feedback Network for Point Cloud Classification. [cls.]
[arXiv] Morphing and Sampling Network for Dense Point Cloud Completion. [oth.]
[arXiv] SGAS: Sequential Greedy Architecture Search. [project] [code] [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] StructEdit: Learning Structural Shape Variations. [project] [rec.]
[arXiv] Grid-GCN for Fast and Scalable Point Cloud Learning. [seg. cls.]