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PSNet : fast data structuring for hierarchical deep learning on point cloud
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Li, Luyang, He, Ligang, Gao, Jinjin and Han, Xie (2022) PSNet : fast data structuring for hierarchical deep learning on point cloud. IEEE Transactions on Circuits and Systems for Video Technology, 32 (10). pp. 6835-6849. doi:10.1109/TCSVT.2022.3171968 ISSN 1051-8215.
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WRAP-PSNet-fast-data-structuring-He-2022.pdf - Accepted Version - Requires a PDF viewer. Download (23Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TCSVT.2022.3171968
Abstract
In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learning models. Due to the disorder nature of the points in a point cloud, the significant time cost may be consumed when grouping and subsampling the points, which consequently results in poor scalability. This paper proposes a fast data structuring method called PSNet (Point Structuring Net). PSNet transforms the spatial features of the points and matches them to the features of local areas in a point cloud. PSNet achieves grouping and sampling at the same time while the existing methods process sampling and grouping in two separate steps (such as using FPS plus kNN). PSNet performs feature transformation pointwise while the existing methods uses the spatial relationship among the points as the reference for grouping. Thanks to these features, PSNet has two important advantages: 1) the grouping and sampling results obtained by PSNet is stable and permutation invariant; and 2) PSNet can be easily parallelized. PSNet can replace the data structuring methods in the mainstream point cloud deep learning models in a plug-and-play manner. We have conducted extensive experiments. The results show that PSNet can improve the training and reasoning speed significantly while maintaining the model accuracy.
Item Type: | Journal Article | ||||||||||||||||||
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Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Deep learning (Machine learning), Computer graphics, Computer vision , Signal processing , Pattern recognition systems | ||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Circuits and Systems for Video Technology | ||||||||||||||||||
Publisher: | IEEE | ||||||||||||||||||
ISSN: | 1051-8215 | ||||||||||||||||||
Official Date: | October 2022 | ||||||||||||||||||
Dates: |
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Volume: | 32 | ||||||||||||||||||
Number: | 10 | ||||||||||||||||||
Number of Pages: | 15 | ||||||||||||||||||
Page Range: | pp. 6835-6849 | ||||||||||||||||||
DOI: | 10.1109/TCSVT.2022.3171968 | ||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
Copyright Holders: | IEEE | ||||||||||||||||||
Date of first compliant deposit: | 25 May 2022 | ||||||||||||||||||
Date of first compliant Open Access: | 25 May 2022 | ||||||||||||||||||
RIOXX Funder/Project Grant: |
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