Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)

Related tags

Deep Learning semantic-segmentation pointclouds 3d-segmentation instance-segmentation 3d-deep-learning 3d-detection 3d-classification 3d-tracking
Overview

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Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)

This is the official repository of Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI), a comprehensive survey of recent progress in deep learning methods for point clouds. For details, please refer to:

Deep Learning for 3D Point Clouds: A Survey

Yulan Guo∗, Hanyun Wang∗, Qingyong Hu∗, Hao Liu∗, Li Liu, and Mohammed Bennamoun.
(* indicates equal contribution)

[Paper] [Blog]

Introduction

We present a comprehensive review of recent deep learning methods for point clouds. It covers major tasks in 3D point cloud analysis, including 3D shape classification, 3D object detection, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions. 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) Datasets

(2) 3D Shape Classification

Public Datasets

Benchmark Results

(3) 3D Object Detection

Public Datasets

Benchmark Results

(4) 3D Point Cloud Segmentation

Public Datasets

Benchmark Results

Citation

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

@article{guo2020deep,
  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={IEEE transactions on pattern analysis and machine intelligence},
  year={2020},
  publisher={IEEE}
}

Updates

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

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  2. SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds GitHub stars
  3. 3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds GitHub stars
  4. SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration GitHub stars
  5. SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels GitHub stars
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Comments
  • Caption missed in Fig.7

    Caption missed in Fig.7

    Thanks for your meaningful work firstly!

    A little typo. There is a single arrow without caption between 3DBN and PointPillars in the bottom part of figure 7 "Chronological overview of the most relevant deep learning-based 3D object detection methods". It seems to be MVX-Net inferring from other documents.

    Looking forward to your next version！

    opened by HannahJHan 0
  • Something Wrong in Fig3?

    Something Wrong in Fig3?

    Hi,Your survey is very useful for me,but is there something wrong in Fig3 which the part of M should be more short？In Fig3 M seems n-dims ，but after max pool layer M is 1-dim。

    opened by azhua66 0
  • Updated Results of MVCNN

    Updated Results of MVCNN

    Dear authors,

    We had an updated version (results) of MVCNN on ModelNet40 which has the sota results. It seems that it is not included in your paper. It would be great if you could cite our results. More details please check here: https://people.cs.umass.edu/~jcsu/papers/shape_recog Thank you!!

    opened by jongchyisu 1
Owner
Qingyong
Ph.D. student :man_student: in the Department of Computer Science at the University of Oxford :cn:
Qingyong
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