University of Oulu

Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu and M. Bennamoun, "Deep Learning for 3D Point Clouds: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 12, pp. 4338-4364, 1 Dec. 2021, doi: 10.1109/TPAMI.2020.3005434

Deep learning for 3D point clouds : a survey

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Author: Guo, Yulan1,2; Wang, Hanyun3; Hu, Qingyong4;
Organizations: 1School of Electronics and Communication Engineering, Sun Yat-sen University, China
2College of Electronic Science and Technology, National University of Defense Technology
3School of Surveying and Mapping, Information Engineering University, China
4Department of Computer Science, University of Oxford, UK
5College of System Engineering, National University of Defense Technology, China
6Center for Machine Vision and Signal Analysis, University of Oulu, Finland
7Department of Computer Science and Software Engineering, the University of Western Australia, Australia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-03-01


Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

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Series: IEEE transactions on pattern analysis and machine intelligence
ISSN: 0162-8828
ISSN-E: 2160-9292
ISSN-L: 0162-8828
Volume: 43
Issue: 12
Pages: 4338 - 4364
DOI: 10.1109/TPAMI.2020.3005434
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was partially supported by the National Natural Science Foundation of China (No. 61972435, 61602499, 61872379), the Natural Science Foundation of Guangdong Province (2019A1515011271), the Science and Technology Innovation Committee of Shenzhen Municipality (JCYJ20190807152209394), the Australian Research Council (Grants DP150100294 and DP150104251), the China Scholarship Council (CSC) and the Academy of Finland.
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