# 3D-Point-Clouds **Repository Path**: ai-group_2/3D-Point-Clouds ## Basic Information - **Project Name**: 3D-Point-Clouds - **Description**: 🔥3D点云目标检测&语义分割(深度学习)-SOTA方法,代码,论文,数据集等 - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2022-12-01 - **Last Updated**: 2023-02-26 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 3D-Point-Clouds 3D点云SOTA方法,代码,论文,数据集(点云目标检测&分割) 点云处理方法上主要包括两类方法 * 深度学习方法 [`python`] * 目标检测&语义分割&... * 传统上基于规则的方法 [`c++`] * PCL:https://github.com/HuangCongQing/pcl-learning * ROS: https://github.com/HuangCongQing/ROS * Apollo笔记：https://github.com/HuangCongQing/apollo_note @[双愚](https://github.com/HuangCongQing) , 若fork或star请注明来源 ## TODO - [ ] 目标检测最新论文实时更新 - [ ] 语义分割最新论文实时更新 - [x] [【done】目标检测框架(pcdet+mmdetection3d+det3d+paddle3d)文章撰写](https://zhuanlan.zhihu.com/p/569189196?) - [ ] 数据集详细剖析：kitti&waymo - [ ] Apollo学习https://github.com/HuangCongQing/apollo_note ## 目录 #### 0 目标检测框架(pcdet+mmdetection3d+det3d+paddle3d) 代码注解笔记： 1. pcdet:https://github.com/HuangCongQing/pcdet-note 2. mmdetection3d:https://github.com/HuangCongQing/mmdetection3d-note 3. det3d: TODO 4. paddle3d: TODO #### 1 paper(code) * paperswithcode: https://paperswithcode.com/ #### 2 Datasets **[自动驾驶相关数据集调研总结【附下载地址】(更新ing)](https://zhuanlan.zhihu.com/p/551861727)** 数据集基本处理: [数据集标注文件处理](https://github.com/HuangCongQing/Python#%E7%82%B9%E4%BA%91%E7%9B%B8%E5%85%B3%E5%A4%84%E7%90%86) 部分数据下载脚本：https://github.com/HuangCongQing/download_3D_dataset #### 3 点云可视化 点云可视化笔记和代码：https://github.com/HuangCongQing/Point-Clouds-Visualization 3D点云可视化的库有很多，你的选择可能是： - pcl 点云可视化 [`c++`] - ROS topic可视化 [`c++`] [`python`] - open3D [`python`] - mayavi[`python`] - matplolib [`python`] #### 4 点云数据标注 数据标注工具总结：https://github.com/HuangCongQing/data-labeling-tools ## paper(code) ### 3D_Object_Detection * One-stage * Two-stage #### One-stage > Voxel-Net、SECOND、PointPillars、HVNet、DOPS、Point-GNN、SA-SSD、3D-VID、3DSSD * Voxel-Net * SECOND * PointPillars * HVNet * DOPS * Point-GNN * SA-SSD * 3D-VID * 3DSSD #### Two-stage > F-pointNet、F-ConvNet、Point-RCNN、Part-A^2、PV-RCNN、Fast Point RCNN、TANet * F-pointNet * F-ConvNet * Point-RCNN * Part-A^2 * PV-RCNN * Fast Point RCNN * TANet ### 3D_Semantic_Segmentation **PointNet** is proposed to learn per-point features using shared MLPs and global features using symmetrical pooling functions. Based on PointNet, a series of point-based networks have been proposed >Point-based Methods: these methods can be roughly divided into pointwise MLP methods, point convolution methods, RNN-based methods, and graph-based methods #### 1 pointwise MLP methods > PointNet++，PointSIFT，PointWeb，ShellNet，RandLA-Net PointNet++ PointSIFT PointWeb ShellNet RandLA-Net #### 2 point convolution methods > PointCNN PCCN A-CNN ConvPoint pointconv KPConv DPC InterpCNN * PointCNN * PCCN * A-CNN * ConvPoint * pointconv * KPConv * DPC * InterpCNN #### 3 RNN-based methods > G+RCU RSNet 3P-RNN DAR-Net * G+RCU * RSNet * 3P-RNN * DAR-Net #### 4 graph-based methods > DGCNN SPG SSP+SPG PyramNet GACNet PAG HDGCN HPEIN SPH3D-GCN DPAM * DGCNN * SPG * SSP+SPG * PyramNet * GACNet * PAG * HDGCN * HPEIN * SPH3D-GCN * DPAM ### 3D_Instance Segmentation ## Datasets ### 数据集下载 * **shell脚本下载方式: https://github.com/HuangCongQing/download_3D_dataset** - [https://hyper.ai/datasets](https://hyper.ai/datasets) - [https://www.graviti.cn/open-datasets](https://www.graviti.cn/open-datasets) > Graviti 收录了 400 多个高质量 CV 类数据集，覆盖无人驾驶、智慧零售、机器人等多种 AI 应用领域。举两个例子： > 文章> [https://bbs.cvmart.net/topics/3346](https://bbs.cvmart.net/topics/3346) - Google数据集搜索：[https://toolbox.google.com/datasetsearch](https://toolbox.google.com/datasetsearch) - Datahub，分享高质量数据集平台：[https://datahub.io/](https://datahub.io/) - 用于上传和查找数据集的机器学习数据集存储库：[https://www.webdoctx.com/www.mldata.org](https://www.webdoctx.com/www.mldata.org) - datafountain收集数据集：[https://www.datafountain.cn/dataSets](https://www.datafountain.cn/dataSets) - tinymind收集数据集：[https://www.tinymind.cn/sites#group_22](https://www.tinymind.cn/sites#group_22) 看到的一篇文章,里面有介绍很多数据集的：[世界上最有价值的不是石油而是数据(附数据资源下载链接)](https://mp.weixin.qq.com/s/Ao8SO9j2IPurl45Noy1dVw) - [https://www.graviti.cn/open-datasets](https://www.graviti.cn/open-datasets) ## Datasets数据集汇总 [https://github.com/Yochengliu/awesome-point-cloud-analysis#---datasets](https://github.com/Yochengliu/awesome-point-cloud-analysis#---datasets) - **[**[KITTI](http://www.cvlibs.net/datasets/kitti/)] The KITTI Vision Benchmark Suite. [`det.`]**常用 - [[ModelNet](http://modelnet.cs.princeton.edu/)] The Princeton ModelNet . [**`cls.`**] - [[ShapeNet](https://www.shapenet.org/)] A collaborative dataset between researchers at Princeton, Stanford and TTIC. [**`seg.`**] - [[PartNet](https://shapenet.org/download/parts)] The PartNet dataset provides fine grained part annotation of objects in ShapeNetCore. [**`seg.`**] - [[PartNet](http://kevinkaixu.net/projects/partnet.html)] PartNet benchmark from Nanjing University and National University of Defense Technology. [**`seg.`**] - **[**[**S3DIS**](http://buildingparser.stanford.edu/dataset.html#Download)**] The Stanford Large-Scale 3D Indoor Spaces Dataset. [`seg.`]**常用 - [[ScanNet](http://www.scan-net.org/)] Richly-annotated 3D Reconstructions of Indoor Scenes. [**`cls.`** **`seg.`**] - [[Stanford 3D](https://graphics.stanford.edu/data/3Dscanrep/)] The Stanford 3D Scanning Repository. [**`reg.`**] - [[UWA Dataset](http://staffhome.ecm.uwa.edu.au/~00053650/databases.html)] . [**`cls.`** **`seg.`** **`reg.`**] - [[Princeton Shape Benchmark](http://shape.cs.princeton.edu/benchmark/)] The Princeton Shape Benchmark. - [[SYDNEY URBAN OBJECTS DATASET](http://www.acfr.usyd.edu.au/papers/SydneyUrbanObjectsDataset.shtml)] This dataset contains a variety of common urban road objects scanned with a Velodyne HDL-64E LIDAR, collected in the CBD of Sydney, Australia. There are 631 individual scans of objects across classes of vehicles, pedestrians, signs and trees. [**`cls.`** **`match.`**] - [[ASL Datasets Repository(ETH)](https://projects.asl.ethz.ch/datasets/doku.php?id=home)] This site is dedicated to provide datasets for the Robotics community with the aim to facilitate result evaluations and comparisons. [**`cls.`** **`match.`** **`reg.`** **`det`**] - [[Large-Scale Point Cloud Classification Benchmark(ETH)](http://www.semantic3d.net/)] This benchmark closes the gap and provides a large labelled 3D point cloud data set of natural scenes with over 4 billion points in total. [**`cls.`**] - [[Robotic 3D Scan Repository](http://asrl.utias.utoronto.ca/datasets/3dmap/)] The Canadian Planetary Emulation Terrain 3D Mapping Dataset is a collection of three-dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada. - [[Radish](http://radish.sourceforge.net/)] The Robotics Data Set Repository (Radish for short) provides a collection of standard robotics data sets. - [[IQmulus & TerraMobilita Contest](http://data.ign.fr/benchmarks/UrbanAnalysis/#)] The database contains 3D MLS data from a dense urban environment in Paris (France), composed of 300 million points. The acquisition was made in January 2013. [**`cls.`** **`seg.`** **`det.`**] - [[Oakland 3-D Point Cloud Dataset](http://www.cs.cmu.edu/~vmr/datasets/oakland_3d/cvpr09/doc/)] This repository contains labeled 3-D point cloud laser data collected from a moving platform in a urban environment. - [[Robotic 3D Scan Repository](http://kos.informatik.uni-osnabrueck.de/3Dscans/)] This repository provides 3D point clouds from robotic experiments，log files of robot runs and standard 3D data sets for the robotics community. - [[Ford Campus Vision and Lidar Data Set](http://robots.engin.umich.edu/SoftwareData/Ford)] The dataset is collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. - [[The Stanford Track Collection](https://cs.stanford.edu/people/teichman/stc/)] This dataset contains about 14,000 labeled tracks of objects as observed in natural street scenes by a Velodyne HDL-64E S2 LIDAR. - [[PASCAL3D+](http://cvgl.stanford.edu/projects/pascal3d.html)] Beyond PASCAL: A Benchmark for 3D Object Detection in the Wild. [**`pos.`** **`det.`**] - [[3D MNIST](https://www.kaggle.com/daavoo/3d-mnist)] The aim of this dataset is to provide a simple way to get started with 3D computer vision problems such as 3D shape recognition. [**`cls.`**] - [[WAD](http://wad.ai/2019/challenge.html)] [[ApolloScape](http://apolloscape.auto/tracking.html)] The datasets are provided by Baidu Inc. [**`tra.`** **`seg.`** **`det.`**] - [[nuScenes](https://d3u7q4379vrm7e.cloudfront.net/object-detection)] The nuScenes dataset is a large-scale autonomous driving dataset.用过 - [[PreSIL](https://uwaterloo.ca/waterloo-intelligent-systems-engineering-lab/projects/precise-synthetic-image-and-lidar-presil-dataset-autonomous)] Depth information, semantic segmentation (images), point-wise segmentation (point clouds), ground point labels (point clouds), and detailed annotations for all vehicles and people. [[paper](https://arxiv.org/abs/1905.00160)] [**`det.`** **`aut.`**] - [[3D Match](http://3dmatch.cs.princeton.edu/)] Keypoint Matching Benchmark, Geometric Registration Benchmark, RGB-D Reconstruction Datasets. [**`reg.`** **`rec.`** **`oth.`**] - [[BLVD](https://github.com/VCCIV/BLVD)] (a) 3D detection, (b) 4D tracking, (c) 5D interactive event recognition and (d) 5D intention prediction. [[ICRA 2019 paper](https://arxiv.org/abs/1903.06405v1)] [**`det.`** **`tra.`** **`aut.`** **`oth.`**] - [[PedX](https://arxiv.org/abs/1809.03605)] 3D Pose Estimation of Pedestrians, more than 5,000 pairs of high-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)] [**`pos.`** **`aut.`**] - [[H3D](https://usa.honda-ri.com/H3D)] Full-surround 3D multi-object detection and tracking dataset. [[ICRA 2019 paper](https://arxiv.org/abs/1903.01568)] [**`det.`** **`tra.`** **`aut.`**] - [[Argoverse BY ARGO AI]](https://www.argoverse.org/) Two public datasets (3D Tracking and Motion Forecasting) supported by highly detailed maps to test, experiment, and teach self-driving vehicles how to understand the world around them.[[CVPR 2019 paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.html)][**`tra.`** **`aut.`**] - [[Matterport3D](https://niessner.github.io/Matterport/)] RGB-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](https://arxiv.org/abs/1907.04758)] SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Nine categories. [**`seg.`** **`aut.`**] - [[Lyft Level 5](https://level5.lyft.com/dataset/?source=post_page)] Include high quality, human-labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map. [**`det.`** **`seg.`** **`aut.`**] - **[**[**SemanticKITTI**](http://semantic-kitti.org/)**] Sequential Semantic Segmentation, 28 classes, for autonomous driving. All sequences of KITTI odometry labeled. [**[**ICCV 2019 paper**](https://arxiv.org/abs/1904.01416)**] [`seg.` `oth.` `aut.`]**常用 - [[NPM3D](http://npm3d.fr/paris-lille-3d)] The Paris-Lille-3D has been produced by a Mobile Laser System (MLS) in two different cities in France (Paris and Lille). [**`seg.`**] - [[The Waymo Open Dataset](https://waymo.com/open/)] The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions. [**`det.`**] - [[A*3D: An Autonomous Driving Dataset in Challeging Environments](https://github.com/I2RDL2/ASTAR-3D)] A*3D: An Autonomous Driving Dataset in Challeging Environments. [**`det.`**] - [[PointDA-10 Dataset](https://github.com/canqin001/PointDAN)] Domain Adaptation for point clouds. - [[Oxford Robotcar](https://robotcar-dataset.robots.ox.ac.uk/)] The dataset captures many different combinations of weather, traffic and pedestrians. [**`cls.`** **`det.`** **`rec.`**] ### 常用分割数据集 - **[**[**S3DIS**](http://buildingparser.stanford.edu/dataset.html#Download)**] The Stanford Large-Scale 3D Indoor Spaces Dataset. [`seg.`] [`常用`] - **[**[**SemanticKITTI**](http://semantic-kitti.org/)**] Sequential Semantic Segmentation, 28 classes, for autonomous driving. All sequences of KITTI odometry labeled. [**[**ICCV 2019 paper**](https://arxiv.org/abs/1904.01416)**] [`seg.` `oth.` `aut.`] [`常用`] - **Semantic3d** ### 常用分类数据集 todo ### 常用目标检测数据集 - **[**[KITTI](http://www.cvlibs.net/datasets/kitti/)] The KITTI Vision Benchmark Suite. [`det.`]**常用 - [[nuScenes](https://d3u7q4379vrm7e.cloudfront.net/object-detection)] The nuScenes dataset is a large-scale autonomous driving dataset.用过 - [[The Waymo Open Dataset](https://waymo.com/open/)] The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions. [**`det.`**] ## References * https://github.com/timzhang642/3D-Machine-Learning * https://github.com/victorphd/autonomous-vahicles-learning-resource * https://github.com/Yochengliu/awesome-point-cloud-analysis * https://github.com/NUAAXQ/awesome-point-cloud-analysis-2021 * https://github.com/QingyongHu/SoTA-Point-Cloud * https://arxiv.org/abs/1912.12033 : Deep Learning for 3D Point Clouds: A Survey ## License Copyright (c) [双愚](https://github.com/HuangCongQing). All rights reserved. Licensed under the [MIT](./LICENSE) License. --- 微信公众号：**【双愚】**（huang_chongqing） 聊科研技术,谈人生思考,欢迎关注~ ![image](https://user-images.githubusercontent.com/20675770/169835565-08fc9a49-573e-478a-84fc-d9b7c5fa27ff.png) **往期推荐：** 1. [本文不提供职业建议，却能助你一生](https://mp.weixin.qq.com/s/rBR62qoAEeT56gGYTA0law) 2. 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