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https://github.com/huangcongqing/3d-point-clouds

🔥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
https://github.com/huangcongqing/3d-point-clouds
3d-detection 3d-point-cloud 3d-point-clouds 3d-semantic-segmentation cpp dataset deep-learning pcl point-cloud python3 ros ros-melodic sota
Last synced: 2 days ago
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🔥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
Host: GitHub
URL: https://github.com/huangcongqing/3d-point-clouds
Owner: HuangCongQing
License: mit
Created: 2021-01-15T09:38:03.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2024-06-20T10:05:13.000Z (7 months ago)
Last Synced: 2025-01-11T23:09:34.050Z (9 days ago)
Topics: 3d-detection, 3d-point-cloud, 3d-point-clouds, 3d-semantic-segmentation, cpp, dataset, deep-learning, pcl, point-cloud, python3, ros, ros-melodic, sota
Language: Jupyter Notebook
Homepage:
Size: 1.25 MB
Stars: 484
Watchers: 8
Forks: 76
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE
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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)  

* 如有疑问，微信：shuangyu_ai

* 更多自动驾驶相关交流群，欢迎扫码加入：[自动驾驶感知(PCL/ROS+DL)：技术交流群汇总(新版)](https://mp.weixin.qq.com/s?__biz=MzI4OTY1MjA3Mg==&mid=2247486575&idx=1&sn=3145b7a5e9dda45595e1b51aa7e45171&chksm=ec2aa068db5d297efec6ba982d6a73d2170ef09a01130b7f44819b01de46b30f13644347dbf2#rd)

应同学建议，创建了星球 **【自动驾驶感知(PCL/ROS+DL)】** 专注于自动驾驶感知领域，包括传统方法(PCL点云库,ROS)和深度学习（目标检测+语义分割）方法。同时涉及Apollo，Autoware(基于ros2)，BEV感知，三维重建，SLAM(视觉+激光雷达) ，模型压缩（蒸馏+剪枝+量化等），自动驾驶模拟仿真，自动驾驶数据集标注&数据闭环等自动驾驶全栈技术，欢迎扫码二维码加入，一起登顶自动驾驶的高峰！

![image](https://github.com/HuangCongQing/HuangCongQing/assets/20675770/304e0c4d-89d2-4cee-a2a9-3c690611c9d9)

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&多目标跟踪（MOT）

  * [【202212done】目标检测最新论文实时更新](https://zhuanlan.zhihu.com/p/591349104)

  * [【202304done】语义分割最新论文实时更新](https://zhuanlan.zhihu.com/p/591349481)

* 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

  - [x] [【202212done】目标检测最新论文实时更新](https://zhuanlan.zhihu.com/p/591349104)

  - [x] [【202304done】语义分割最新论文实时更新](https://zhuanlan.zhihu.com/p/591349481)

  - [x] [【202209done】目标检测框架(pcdet+mmdetection3d+det3d+paddle3d)文章撰写](https://zhuanlan.zhihu.com/p/569189196?)

  - [x] [【202208done】数据集调研总结](https://zhuanlan.zhihu.com/p/551861727)

  - [x] [【202406done】3D语义分割框架综述(mmdetection3d|OpenPCSeg|Pointcept)](https://zhuanlan.zhihu.com/p/701605684?)

  - [ ] 数据集详细剖析：kitti&waymo&nuScenes

  - [ ] Apollo学习https://github.com/HuangCongQing/apollo_note

## 目录

#### 0 目标检测框架(pcdet+mmdetection3d+det3d+paddle3d)

> [【202209done】目标检测框架(pcdet+mmdetection3d+det3d+paddle3d)文章撰写](https://zhuanlan.zhihu.com/p/569189196?)

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

- [x]  [**>>>目标检测最新论文实时更新**](https://zhuanlan.zhihu.com/p/591349104)

* 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

- [x]  [**>>>语义分割最新论文实时更新**](https://zhuanlan.zhihu.com/p/591349481)

**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

* https://github.com/zhulf0804/3D-PointCloud

## 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)

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本人创建星球 **【自动驾驶感知(PCL/ROS+DL)】** 专注于自动驾驶感知领域，包括传统方法(PCL点云库,ROS)和深度学习（目标检测+语义分割）方法。同时涉及Apollo，Autoware(基于ros2)，BEV感知，三维重建，SLAM(视觉+激光雷达) ，模型压缩（蒸馏+剪枝+量化等），自动驾驶模拟仿真，自动驾驶数据集标注&数据闭环等自动驾驶全栈技术，欢迎扫码二维码加入，一起登顶自动驾驶的高峰！

![image](https://github.com/HuangCongQing/HuangCongQing/assets/20675770/304e0c4d-89d2-4cee-a2a9-3c690611c9d9)