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awesome-pointcloud-processing
awesome PointCloud processing algorithm
https://github.com/Hardy-Uint/awesome-pointcloud-processing
Last synced: about 8 hours ago
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点云去噪&滤波
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点云线、面拟合
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点云识别&分类
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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
- Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
- [CVPR2019
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点云匹配&配准&对齐&注册
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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
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点云数据集
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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
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点云分割
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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
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点云三维重建
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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
- 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.
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点云其它
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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.
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点云标注工具
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点云匹配质量评估
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商用
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3D/点云目标检索
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商用
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Programming Languages
Categories