README.md

Deep Learning for 3D Point Clouds: A Survey

Yulan Guo^∗, Hanyun Wang^∗, Qingyong Hu^∗, Hao Liu^∗, Li Liu, and Mohammed Bennamoun.

Introduction

We present a comprehensive review of recent deep learning methods for point clouds. Please see our arXiv tech report. This repository provides the results of existing methods on several public benchmarks. It covers three major tasks, including 3D shape classification, 3D object detection, and 3D point cloud segmentation. Please feel free to contact me or create an issue on this page if you have new results to add or any suggestions!

We will update this page on a regular basis! So stay tuned~ 🎉🎉🎉

(1) 3D Shape Classification

Public Datasets

• ModelNet (CVPR'15) [paper] [project page]
  • ModelNet10 [data] [results]
  • ModelNet40 [data] [results]
• PartNet (CVPR'19) [paper] [data] [project page]
• ScanObjectNN (ICCV'19) [paper] [data] [project page]

Benchmark Results

(2) 3D Object Detection

Public Datasets

• KITTI (CVPR'12) [paper] [project page]
  • 3D objecct detection [data] [results]
  • BEV [data] [results]
• ApolloScape (TPAMI'19) [paper] [data] [results]
• Argoverse (CVPR'19) [paper] [data] [project page]
• A*3D (arXiv'19) [paper] [data] [project page]
• Waymo (arXiv'19) [paper] [data] [project page]

Benchmark Results

(3) 3D Point Cloud Segmentation

Public Datasets

• Semantic3D (ISPRS'17) [paper] [project page]
  • semantic-8 [data] [results]
  • reduced-8 [data] [results]
• S3DIS (CVPR'17) [paper] [data] [project page]
• ScanNet (CVPR'17) [paper] [data] [project page] [results]
• NPM3D (IJRR'18) [paper] [data] [project page] [results]
• SemanticKITTI (ICCV'19) [paper] [data] [project page] [results]

Benchmark Results

Citation

If you find our work useful in your research, please consider citing:

@article{guo2019deep,
  title={Deep Learning for 3D Point Clouds: A Survey},
  author={Guo, Yulan and Wang, Hanyun and Hu, Qingyong and Liu, Hao and Liu, Li and Bennamoun, Mohammed},
  journal={arXiv preprint arXiv:1912.12033},
  year={2019}
}

Updates

• 26/02/2020: Adding the dataset information
• 27/12/2019: Initial release.