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awesome-pointcloud-processing

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

Semantic.editor
基于K-近邻点云去噪算法的研究与改进
Point cloud denoising based on tensor Tucker decomposition
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
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
3D ShapeNets: A Deep Representation for Volumetric Shapes
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
[ICCV2017
[ICRA2017
[IROS2017
[CVPR2018
[CVPR2018 - Net: Self-Organizing Network for Point Cloud Analysis.
[CVPR2018 - Scale Place Recognition.
[CVPR2018
[CVPR2019
[MM - Modal Joint Networks for 3D Shape Recognition.
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
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
[CVPR2019 - Based Randomized Approach for Robust Point Cloud Registration without Correspondences.
[CVPR2019
[CVPR - Set Registration using Gaussian Filter and Twist Parameterization.
[CVPR2019
[ICCV2019 - to-End Deep Neural Network for 3D Point Cloud Registration.
[ICCV2019
[ICRA2019 - MatchNet: Learning to Match Keypoints across 2D Image and 3D Point Cloud.
[CVPR2019
[CVPR2019
[ICCV2019
[ICRA2019 - overlap 3-D point cloud registration for outlier rejection.
[IROS2017
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
[ICRA2017
[3DV2017
[CVPR2018
[CVPR2018
[CVPR2018 - scale Point Cloud Semantic Segmentation with Superpoint Graphs.
[ECCV2018
[CVPR2019 - Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields.
[CVPR2019 - grained and Hierarchical Shape Segmentation.
[ICCV2019 - Task Metric Learning.
[IROS2019
人脸识别
改进的点云数据三维重建算法
Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity,CVPR2017
[ICCV2017
[ICCV2017
[ECCV2018
[ECCV2018
[AAAI2018
[CVPR2019 - Scale Outdoor Scenes.
[AAAI2019
[MM - encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention.
[CVPR2018 - Scale 3D Point Clouds.
[ICML2018
[3DV
[CVPR2019 - scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding.
[CVPR2019 - Invariant Representation for Point Cloud Analysis.
[ICCV2019 - Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis.
[ICRA2019
[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.
[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)]
[3D Match - D Reconstruction Datasets.
[BLVD
[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)]
[H3D - surround 3D multi-object detection and tracking dataset. [[ICRA 2019 paper](https://arxiv.org/abs/1903.01568)]
[Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)]
[SynthCity
[Lyft Level 5 - labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map.
[SemanticKITTI
[NPM3D - Lille-3D has been produced by a Mobile Laser System (MLS) in two different cities in France (Paris and Lille).
[The Waymo Open Dataset - driving cars in a wide variety of conditions.
[A*3D: An Autonomous Driving Dataset in Challeging Environments
[PointDA-10 Dataset
[Oxford Robotcar

Programming Languages

Python 2 C++ 1 JavaScript 1

Keywords

semantic-scene-understanding 1 3d-reconstruction 1