garry1ng / 3D-PointCloud

Papers and Datasets about Point Cloud.

3D - Point Cloud

Paper list and Datasets about Point Cloud. Datasets can be found in Datasets.md.

Survey papers

Deep Learning for 3D Point Clouds: A Survey [TPAMI 2020]

2020

ICLR
ECCV
- A Closer Look at Local Aggregation Operators in Point Cloud Analysis [cls, seg; Code]
- Multimodal Shape Completion via Conditional Generative Adversarial Networks [completion; PyTorch]
- GRNet: Gridding Residual Network for Dense Point Cloud Completion [completion; PyTorch]
- 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection [det]
- SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds [det; Github]
- Pillar-based Object Detection for Autonomous Driving [det, autonomous driving; Tensorflow]
- EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection [det; PyTorch]
- Finding Your (3D) Center: 3D Object Detection Using a Learned Loss [det; Tensorflow]
- Weakly Supervised 3D Object Detection from Lidar Point Cloud [det; PyTorch]
- H3DNet: 3D Object Detection Using Hybrid Geometric Primitives [det; Tensorflow]
- Generative Sparse Detection Networks for 3D Single-shot Object Detection [det; Github]
- Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution [seg, det; PyTorch]
- DeepGMR: Learning Latent Gaussian Mixture Models for Registration [registration; PyTorch]
- Quaternion Equivariant Capsule Networks for 3D Point Clouds [PyTorch]
- PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding [unsupervised; cls, seg, det]
- Convolutional Occupancy Networks [reconstruction; PyTorch]
- Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration [registration; PyTorch]
- Progressive Point Cloud Deconvolution Generation Network [generation; github]
- Reinforced Axial Refinement Network for Monocular 3D Object Detection [det, monocular]
- Monocular 3D Object Detection via Feature Domain Adaptation [det, monocular]
- Improving 3D Object Detection through Progressive Population Based Augmentation [det]
- An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds [det]
- Rotation-robust Intersection over Union for 3D Object Detection
CVPR
- Deep Global Registration [registration; PyTorch]
- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection [det; code]
- SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud [det; PtTorch]
- 3DRegNet: A Deep Neural Network for 3D Point Registration [registration; Tensorflow]
- MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment [non-rigid alignment]
- SampleNet: Differentiable Point Cloud Sampling [cls, registration, reconstruction; PyTorch]
- Learning multiview 3D point cloud registration [multiview registration; PyTorch]
- Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences [registration; PyTorch]
- PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling [cls, seg; Tensorflow]
- Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [unsupervised; cls; PyTorch]
- Grid-GCN for Fast and Scalable Point Cloud Learning [cls, seg; mxnet]
- FPConv: Learning Local Flattening for Point Convolution [cls, seg; PyTorch]
- PointAugment: an Auto-Augmentation Framework for Point Cloud Classification [cls, retrieval; github]
- RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds [seg; Tensorflow]
- Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels [weakly supervised; seg; Tensorflow]
- PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation [seg; PyTorch]
- Learning to Segment 3D Point Clouds in 2D Image Space [seg; Keras]
- PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation [seg; PyTorch]
- D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [registration; Tensorflow]
- RPM-Net: Robust Point Matching using Learned Features [registration; PyTorch]
- Cascaded Refinement Network for Point Cloud Completion [completion; Tensorflow]
- P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds [tracking; PyTorch]
- An Efficient PointLSTM for Point Clouds Based Gesture Recognition [gesture; PyTorch]
AAAI
- MSN: Morphing and Sampling Network for Dense Point Cloud Completion [completion; PyTorch]
- TANet: Robust 3D Object Detection from Point Clouds with Triple Attention [det; PyTorch]
- Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling [cls, seg]
- Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution [cls, seg, matching]
Others
- Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds [seg, cls; Project; ICRA]
- Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features [registration; Remote Sensing]
arXiv
- Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation [seg]
- A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds [det]
- TEASER: Fast and Certifiable Point Cloud Registration [registration; Github]

2019

NeurIPS
- Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds [seg; Tensorflow]
- PRNet: Self-Supervised Learning for Partial-to-Partial Registration [registration, cls; PyTorch]
- Point-Voxel CNN for Efficient 3D Deep Learning [seg, det; PyTorch]
ICCV
- SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences [dataset]
- MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences [cls, seg, flow estimation; Tensorflow]
- DeepGCNs: Can GCNs Go as Deep as CNNs? [seg; Tensorflow]
- VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation [seg; Github]
- Interpolated Convolutional Networks for 3D Point Cloud Understanding [cls, seg]
- Dynamic Points Agglomeration for Hierarchical Point Sets Learning [cls, seg]
- ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics [cls, seg; Tensorflow]
- Fast Point R-CNN [det]
- Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data [dataset; cls; Tensorflow]
- KPConv: Flexible and Deformable Convolution for Point Clouds [cls, seg; code]
- Fully Convolutional Geometric Features [match; PyTorch]
- Deep Closest Point: Learning Representations for Point Cloud Registration [registration; PyTorch]
- DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration [registration]
- Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation [seg]
- DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing [cls, retrieval, seg, normal estimation; PyTorch]
- DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense [cls]
- Efficient Learning on Point Clouds with Basis Point Sets [cls, registration; PyTorch]
- PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows [generation, reconstruction; Pytorch
- PU-GAN: a Point Cloud Upsampling Adversarial Network [upsampling, reconstruction; Project]
- 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition [retrieval, place recognition]
- Deep Hough Voting for 3D Object Detection in Point Clouds [det; PyTorch]
- Exploring the Limitations of Behavior Cloning for Autonomous Driving [autonomous driving; Pytorch]
CVPR
- JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields [seg; PyTorch]
- Learning Transformation Synchronization [reconstruction; PyTorch]
- 3D Local Features for Direct Pairwise Registration [registration]
- DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds [registration; Github]
- Relation-Shape Convolutional Neural Network for Point Cloud Analysis [cls, seg, normal estimation; PyTorch]
- Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN [cls, det; Tensorflow]
- 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks [seg; PyTorch]
- PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval [retrieval; Tensorflow]
- Attentional PointNet for 3D-Object Detection in Point Clouds [det; PyTorch]
- Octree guided CNN with Spherical Kernels for 3D Point Clouds [cls, seg; Github]
- A-CNN: Annularly Convolutional Neural Networks on Point Clouds [cls, seg; Tensorflow]
- ClusterNet: Deep Hierarchical Cluster Network with Rigorously Rotation-Invariant Representation for Point Cloud Analysis [cls]
- Graph Attention Convolution for Point Cloud Semantic Segmentation [seg; PyTorch-unofficial]
- PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing [seg, cls; PyTorch]
- Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling [cls, seg, gesture]
- Learning to Sample [cls, retrieval, reconstruction; Tensorflow]
- PointConv: Deep Convolutional Networks on 3D Point Clouds [cls, seg; Tensorflow]
- The Perfect Match: 3D Point Cloud Matching With Smoothed Densities [match; code]
- PointNetLK: Point Cloud Registration using PointNet [registration; PyTorch]
- PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud [det; PyTorch]
- PointPillars: Fast Encoders for Object Detection From Point Clouds [det; Pytorch]
- Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [depth estimation, det; github]
- ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving [dataset, autonomous driving]
- Stereo R-CNN based 3D Object Detection for Autonomous Driving [det, autonomous driving; github]
- Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [det, autonomous driving; Tesorflow]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [det]
- GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving [det, autonomous driving]
- L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving [autonomous driving]
Others
- Dynamic Graph CNN for Learning on Point Clouds [cls, seg; Github; TOG]
- SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud [seg; Tensorflow; ICRA]
- RangeNet++: Fast and Accurate LiDAR Semantic Segmentation [seg; PyTorch; IROS]
arXiv
- LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer [cls, seg; Tensorflow]
- Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving [autonomous driving]

2018

CVPR
- Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling [cls, seg; Code]
- Tangent Convolutions for Dense Prediction in 3D [seg; Tensorflow]
- PointGrid: A Deep Network for 3D Shape Understanding [cls, seg; Tensorflow]
- 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks [seg; Github]
- Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs [seg; PyTorch]
- SPLATNet: Sparse Lattice Networks for Point Cloud Processing [seg; Caffe]
- Pointwise Convolutional Neural Networks [cls, seg; Tensorflow]
- SO-Net: Self-Organizing Network for Point Cloud Analysis [autoencoder, cls, seg; PyTorch]
- Recurrent Slice Networks for 3D Segmentation of Point Clouds [seg; PyTorch]
- PPFNet: Global Context Aware Local Features for Robust 3D Point Matching [registration]
- PIXOR: Real-Time 3D Object Detection From Point Clouds [det; PyTorch]
- Frustum PointNets for 3D Object Detection From RGB-D Data [det; Tensorflow]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [det]
- 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare [reconstruction]
- Multi-Level Fusion Based 3D Object Detection From Monocular Images [det]
ECCV
- Learning and Matching Multi-View Descriptors for Registration of Point Clouds [registration]
- Local Spectral Graph Convolution for Point Set Feature Learning [cls, seg]
- 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation [seg]
- Fully-Convolutional Point Networks for Large-Scale Point Clouds [seg, captioning; Tensorflow]
- PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors [registration; PyTorch]
- Deep Continuous Fusion for Multi-Sensor 3D Object Detection [det]
- 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [match, registration; Tensorflow]
- Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving [autonomous driving]
Others
- PointCNN: Convolution On X -Transformed Points [cls, seg; Tensorflow; NeurIPS]
- Guaranteed Outlier Removal for Point Cloud Registration with Correspondences [registration; TPAMI]
- Second: Sparsely embedded convolutional detection [det; Sensors]
- Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving [det, autonomous driving; IEEE Robotics and Automation Letters]
- HDNET: Exploiting HD Maps for 3D Object Detection [det, autonomous driving; CoRL]
- Joint 3D Proposal Generation and Object Detection from View Aggregation [det, autonomous driving; IROS]
- Flex-Convolution(Million-Scale Point-Cloud Learning Beyond Grid-Worlds) [cls, seg; Tensorflow; ACCV]
- SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud [seg; Tensorflow; ICRA]
- Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods [seg; 3DV]
arXiv
- Point Convolutional Neural Networks by Extension Operators [cls, seg, normal estimation; Tensorflow]
- PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation [seg; Tensorflow]
- Roarnet: A robust 3d object detection based on region approximation refinement [det]
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds [det; PyTorch]
- Classification of Point Cloud Scenes with Multiscale Voxel Deep Network [seg]

2017

CVPR
- Multi-View 3D Object Detection Network for Autonomous Driving [det, autonomous driving; Tensorflow]
- Deep MANTA: A Coarse-To-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis From Monocular Image [autonomous driving]
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [cls, seg; Tensorflow]
- 3D Bounding Box Estimation Using Deep Learning and Geometry [det]
- OctNet: Learning Deep 3D Representations at High Resolutions [cls, seg, orientation estimation; PyTorch]
- 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions [match, registration; project]
- 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder [registration; github]
ICCV
- Learning Compact Geometric Features [registration; Github]
- 2D-Driven 3D Object Detection in RGB-D Images [det]
TPAMI
- 3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection [det, autonomous driving]
NIPS
- Pointnet++: Deep hierarchical feature learning on point sets in a metric space [cls, seg; Tensorflow]
Others
- Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks [det; ICRA]
- 3d fully convolutional network for vehicle detection in point cloud [det; IROS; Tensorflow]
- SEGCloud: Semantic Segmentation of 3D Point Clouds [seg; 3DV]

Before 2016

2016
- Fast Global Registration [registration; ECCV; Github]
- Monocular 3D Object Detection for Autonomous Driving [CVPR]
- Volumetric and Multi-View CNNs for Object Classification on 3D Data [CVPR]
- Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients [CVPR]
- Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images [CVPR]
- Fpnn: Field probing neural networks for 3d data [NIPS]
- Vehicle Detection from 3D Lidar Using Fully Convolutional Network [RSS]
2015
- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration [registration; TPAMI; Github]
- 3D ShapeNets: A Deep Representation for Volumetric Shapes [CVPR]
- SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite [CVPR]
- Data-Driven 3D Voxel Patterns for Object Category Recognition [CVPR]
- Multi-view convolutional neural networks for 3d shape recognition [ICCV]
- 3d object proposals for accurate object class detection [NIPS]
- Voting for Voting in Online Point Cloud Object [RSS]
- Voxnet: A 3d convolutional neural network for real-time object recognition [IROS]
2014
- Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects [CVPR]
- Sliding shapes for 3d object detection in depth images [ECCV]

2012
- Are we ready for autonomous driving? the kitti vision benchmark suite [CVPR]
2009
- Fast point feature histograms (FPFH) for 3D registration [registration; ICRA]
1992
- A method for registration of 3-D shapes [registration; TPAMI]
1987
- Least-squares fitting of two 3-D point sets [registration; TPAMI]

Reference

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Papers and Datasets about Point Cloud.