Let's study Point Cloud together.
- Deep Learning for 3D Point Clouds: A Survey [TPAMI 2020]
- A Survey on Deep Geometry Learning: From a Representation Perspective [arXiv 2020]
- ECCV
- A Closer Look at Local Aggregation Operators in Point Cloud Analysis [Code]
- Multimodal Shape Completion via Conditional Generative Adversarial Networks [PyTorch]
- GRNet: Gridding Residual Network for Dense Point Cloud Completion [PyTorch]
- 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
- Progressive Point Cloud Deconvolution Generation Network [github]
- CVPR
- Learning multiview 3D point cloud registration [PyTorch]
- Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences [PyTorch]
- PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling [Tensorflow]
- Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [PyTorch]
- Grid-GCN for Fast and Scalable Point Cloud Learning [mxnet]
- FPConv: Learning Local Flattening for Point Convolution [PyTorch]
- PointAugment: an Auto-Augmentation Framework for Point Cloud Classification [github]
- RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds [Tensorflow]
- Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels [Tensorflow]
- PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation [PyTorch]
- Learning to Segment 3D Point Clouds in 2D Image Space [Keras]
- PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation [PyTorch]
- D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [Tensorflow]
- RPM-Net: Robust Point Matching using Learned Features [PyTorch]
- Cascaded Refinement Network for Point Cloud Completion [Tensorflow]
- P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds [PyTorch]
- An Efficient PointLSTM for Point Clouds Based Gesture Recognition [PyTorch]
- AAAI
- MSN: Morphing and Sampling Network for Dense Point Cloud Completion [PyTorch]
- TANet: Robust 3D Object Detection from Point Clouds with Triple Attention [PyTorch]
- Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
- Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution
- NeurIPS
- ICCV
- Deep Closest Point: Learning Representations for Point Cloud Registration [PyTorch]
- DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
- Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
- DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
- Efficient Learning on Point Clouds with Basis Point Sets
- PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows [Pytorch
- PU-GAN: a Point Cloud Upsampling Adversarial Network [Project]
- 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition
- Deep Hough Voting for 3D Object Detection in Point Clouds [PyTorch]
- Exploring the Limitations of Behavior Cloning for Autonomous Driving [Pytorch]
- PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows [PyTorch]
- CVPR
- PointNetLK: Point Cloud Registration using PointNet [PyTorch]
- PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud [PyTorch]
- PointPillars: Fast Encoders for Object Detection From Point Clouds [Pytorch]
- Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [github]
- ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving
- Stereo R-CNN based 3D Object Detection for Autonomous Driving [github]
- Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
- GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
- L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving
- Others
- arXiv
- CVPR
- PIXOR: Real-Time 3D Object Detection From Point Clouds
- Frustum PointNets for 3D Object Detection From RGB-D Data
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare
- Multi-Level Fusion Based 3D Object Detection From Monocular Images
- ECCV
- TPAMI
- IROS
- Others
- Second: Sparsely embedded convolutional detection [Sensors]
- Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving [IEEE Robotics and Automation Letters]
- HDNET: Exploiting HD Maps for 3D Object Detection [CoRL]
- arXiv
- CVPR
- Multi-View 3D Object Detection Network for Autonomous Driving
- Deep MANTA: A Coarse-To-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis From Monocular Image
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 3D Bounding Box Estimation Using Deep Learning and Geometry
- Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes From 2D Ones in RGB-Depth Images [Caffe]
- OctNet: Learning Deep 3D Representations at High Resolutions
- ICCV
- TPAMI
- NIPS
- Others
- Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks [ICRA]
- 3d fully convolutional network for vehicle detection in point cloud [IROS]
- Vehicle detection and localization on bird's eye view elevation images using convolutional neural network [SSRR]
- Depthcn: vehicle detection using 3d-lidar and convnet [ITSC]
- 2016
- 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
- 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
| Dataset | Paper | Confonerence | Github | Others |
|---|---|---|---|---|
| Kitti | Are we ready for autonomous driving? the KITTI vision benchmark suite | CVPR 2012 | ||
| ModelNet | 3d shapenets: A deep representation for volumetric shapes | CVPR 2015 | HDF5 | |
| ShapeNet Part | A Scalable Active Framework for Region Annotation in 3D Shape Collections | SIGGRAPH Asia 2016 | ||
| 3D Semantic Parsing Datasets | 3D Semantic Parsing of Large-Scale Indoor Spaces | CVPR 2016 | ||
| ScanNet | Scannet: Richly-annotated 3d reconstructions of indoor scenes | CVPR 2017 | Github | |
| SUN RGB-D | Sun rgb-d: A rgb-d scene understanding benchmark suite | CVPR 2015 |
- https://github.com/amusi/ECCV2020-Code
- https://github.com/amusi/CVPR2020-Code
- https://github.com/extreme-assistant/CVPR2020-Paper-Code-Interpretation
- https://github.com/Yochengliu/awesome-point-cloud-analysis
- http://bbs.cvmart.net/topics/302/cvpr2019paper
- https://github.com/extreme-assistant/iccv2019
- https://github.com/amusi/daily-paper-computer-vision
- http://openaccess.thecvf.com/menu.py
- https://arxiv.org/