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awesome-pointcloud-processing

awesome PointCloud processing algorithm
https://github.com/Hardy-Uint/awesome-pointcloud-processing

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点云去噪&滤波
- 商用
点云线、面拟合
- 商用
  - 基于最小二乘的点云叶面拟合算法研究
  - 点云曲面边界线的提取
点云识别&分类
- 商用
  - 3D ShapeNets: A Deep Representation for Volumetric Shapes
  - Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
  - [ICCV2017
  - [IROS2017
  - [CVPR2018
  - [CVPR2018 - Net: Self-Organizing Network for Point Cloud Analysis.
  - [CVPR2018 - Scale Place Recognition.
  - [CVPR2018
  - [CVPR2018 - Scale Place Recognition.
  - [CVPR2018
  - [MM - Modal Joint Networks for 3D Shape Recognition.
  - Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
  - [CVPR2019
点云匹配&配准&对齐&注册
- 商用
  - An ICP variant using a point-to-line metric
  - Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
  - 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
  - [CVPR2018
  - [CVPR2018
  - [CVPR2018
  - [ECCV2018 - View Descriptors for Registration of Point Clouds.
  - [ECCV2018 - Net: Weakly Supervised Local 3D Features for Point Cloud Registration.
  - [ECCV2018
  - [IROS2018
  - [CVPR2019
  - [ICCV2019
  - [ICCV2019
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - [IROS2018
  - [IROS2018
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - Generalized-ICP
  - Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration
  - Metric-Based Iterative Closest Point Scan Matching for Sensor Displacement Estimation
  - NICP: Dense Normal Based Point Cloud Registration
  - [CVPR2019
  - [CVPR2019 - Based Randomized Approach for Robust Point Cloud Registration without Correspondences.
  - [CVPR2019
  - [CVPR - Set Registration using Gaussian Filter and Twist Parameterization.
  - [ICCV2019 - to-End Deep Neural Network for 3D Point Cloud Registration.
  - [ICRA2019 - MatchNet: Learning to Match Keypoints across 2D Image and 3D Point Cloud.
  - [ICRA2019 - overlap 3-D point cloud registration for outlier rejection.
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
  - An ICP variant using a point-to-line metric
点云数据集
- 商用
  - [WAD
  - [KITTI
  - [ModelNet
  - [ShapeNet
  - [PartNet
  - [PartNet
  - [S3DIS - Scale 3D Indoor Spaces Dataset.
  - [ScanNet - annotated 3D Reconstructions of Indoor Scenes.
  - [Stanford 3D
  - [UWA Dataset
  - [Princeton Shape Benchmark
  - [SYDNEY URBAN OBJECTS DATASET - 64E LIDAR, collected in the CBD of Sydney, Australia. There are 631 individual scans of objects across classes of vehicles, pedestrians, signs and trees.
  - [ASL Datasets Repository(ETH)
  - [Large-Scale Point Cloud Classification Benchmark(ETH)
  - [Robotic 3D Scan Repository - dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada.
  - [Radish
  - [IQmulus & TerraMobilita Contest
  - [Oakland 3-D Point Cloud Dataset - D point cloud laser data collected from a moving platform in a urban environment.
  - [Robotic 3D Scan Repository
  - [Ford Campus Vision and Lidar Data Set - 250 pickup truck.
  - [The Stanford Track Collection - 64E S2 LIDAR.
  - [PASCAL3D+
  - [3D MNIST
  - [WAD
  - [nuScenes - scale autonomous driving dataset.
  - [3D Match - D Reconstruction Datasets.
  - [NPM3D - Lille-3D has been produced by a Mobile Laser System (MLS) in two different cities in France (Paris and Lille).
  - [Oxford Robotcar
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [Ford Campus Vision and Lidar Data Set - 250 pickup truck.
  - [WAD
  - [PartNet
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [PreSIL - wise segmentation (point clouds), ground point labels (point clouds), and detailed annotations for all vehicles and people. [[paper](https://arxiv.org/abs/1905.00160)]
  - [PedX - resolution (12MP) stereo images and LiDAR data along with providing 2D and 3D labels of pedestrians. [[ICRA 2019 paper](https://arxiv.org/abs/1809.03605)]
  - [SynthCity
  - [Lyft Level 5 - labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map.
  - [SemanticKITTI
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
  - [WAD
点云分割
- 商用
  - [3DV2017
  - [CVPR2018
  - [CVPR2018
  - [CVPR2018 - scale Point Cloud Semantic Segmentation with Superpoint Graphs.
  - [ECCV2018
  - [ICCV2019 - Task Metric Learning.
  - [ICRA2017
  - 基于局部表面凸性的散乱点云分割算法研究
  - 三维散乱点云分割技术综述
  - 基于聚类方法的点云分割技术的研究
  - SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor
  - From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds
  - Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation
  - JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds
  - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
  - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
  - [CVPR2019 - Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields.
  - [CVPR2019 - grained and Hierarchical Shape Segmentation.
  - [IROS2019
点云三维重建
- 商用
  - Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity,CVPR2017
  - [ICCV2017
  - [ICCV2017
  - [ECCV2018
  - [ECCV2018
  - [AAAI2018
  - [CVPR2019 - Scale Outdoor Scenes.
  - [AAAI2019
  - [ICCV2017
  - [ECCV2018
  - [MM - encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention.
  - 改进的点云数据三维重建算法
  - [MM - encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention.
点云其它
- 商用
  - [CVPR2018 - Scale 3D Point Clouds.
  - [ICML2018
  - [3DV
  - [CVPR2019 - Invariant Representation for Point Cloud Analysis.
  - [ICCV2019 - Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis.
  - [ICRA2019
  - [CVPR2019 - scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding.
点云标注工具
- 开源
  - Semantic.editor
点云匹配质量评估
- 商用
  - [IROS2017
3D/点云目标检索
- 商用
  - 人脸识别

Programming Languages

Categories

点云匹配&配准&对齐&注册 85 点云数据集 81 点云分割 19 点云三维重建 13 点云识别&分类 13 点云其它 7 点云去噪&滤波 3 点云线、面拟合 2 点云标注工具 1 点云匹配质量评估 1 3D/点云目标检索 1

Sub Categories

商用 225 开源 1