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An end-to-end dynamic point cloud geometry compression in latent space

Bailin Yang, Zhaoyi Jiang Orcid Logo, Guoliang Wang, Gary Tam Orcid Logo, Chao Song, Frederick W.B. Li

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Swansea University Author: Gary Tam Orcid Logo

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DOI (Published version): 10.1016/j.displa.2023.102528

Abstract

Dynamic point clouds are widely used for 3D data representation in various applications such as immersive and mixed reality, robotics and autonomous driving. However, their irregularity and large scale make efficient compression and transmission a challenge. Existing methods require high bitrates to...

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Published in: Displays
ISSN: 0141-9382
Published: https://www.easychair.org/smart-program/CGI2023/2023-08-28.html#talk:235847 Elsevier BV
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URI: https://cronfa.swan.ac.uk/Record/cronfa64182
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spelling v2 64182 2023-08-30 An end-to-end dynamic point cloud geometry compression in latent space e75a68e11a20e5f1da94ee6e28ff5e76 0000-0001-7387-5180 Gary Tam Gary Tam true false 2023-08-30 SCS Dynamic point clouds are widely used for 3D data representation in various applications such as immersive and mixed reality, robotics and autonomous driving. However, their irregularity and large scale make efficient compression and transmission a challenge. Existing methods require high bitrates to encode point clouds since temporal correlation is not well considered. This paper proposes an end-to-end dynamic point cloud compression network that operates in latent space, resulting in more accurate motion estimation and more effective motion compensation. Specifically, a multi-scale motion estimation network is introduced to obtain accurate motion vectors. Motion information computed at a coarser level is upsampled and warped to the finer level based on cost volume analysis for motion compensation. Additionally, a residual compression network is designed to mitigate the effects of noise and inaccurate predictions by encoding latent residuals, resulting in smaller conditional entropy and better results. The proposed method achieves an average 12.09% and 14.76% (D2) BD-Rate gain over state-of-the-art Deep Dynamic Point Cloud Compression (D-DPCC) in experimental results. Compared to V-PCC, our framework showed an average improvement of 81.29% (D1) and 77.57% (D2). Journal Article Displays 102528 Elsevier BV https://www.easychair.org/smart-program/CGI2023/2023-08-28.html#talk:235847 0141-9382 Dynamic point clouds compression, Geometry encoding, Latent scene flow, Deep entropy model 0 0 0 0001-01-01 10.1016/j.displa.2023.102528 http://dx.doi.org/10.1016/j.displa.2023.102528 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This research was partially supported by Zhejiang Province Natural Science Foundation No. LY21F020013, LY22F020013, the National Natural Science Foundation of China No. 62172366. Gary Tam is supported by the Royal Society grant IEC/NSFC/211159. For the purpose of Open Access the author has applied a CC BY copyright licence to any Author Accepted Manuscript version arising from this submission. 2023-09-25T16:47:01.0226274 2023-08-30T15:53:10.5321838 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Bailin Yang 1 Zhaoyi Jiang 0000-0001-5347-7935 2 Guoliang Wang 3 Gary Tam 0000-0001-7387-5180 4 Chao Song 5 Frederick W.B. Li 6 64182__28401__369152392d854ae1897036e8bd1c4a54.pdf cgi2023_elsarticle_DISPLA__Copy_accepted.pdf 2023-08-30T15:59:11.1996250 Output 1502337 application/pdf Accepted Manuscript true For the purpose of Open Access the author has applied a CC BY copyright licence to any Author Accepted Manuscript version arising from this submission. true eng http://creativecommons.org/licenses/by/4.0/
title An end-to-end dynamic point cloud geometry compression in latent space
spellingShingle An end-to-end dynamic point cloud geometry compression in latent space
Gary Tam
title_short An end-to-end dynamic point cloud geometry compression in latent space
title_full An end-to-end dynamic point cloud geometry compression in latent space
title_fullStr An end-to-end dynamic point cloud geometry compression in latent space
title_full_unstemmed An end-to-end dynamic point cloud geometry compression in latent space
title_sort An end-to-end dynamic point cloud geometry compression in latent space
author_id_str_mv e75a68e11a20e5f1da94ee6e28ff5e76
author_id_fullname_str_mv e75a68e11a20e5f1da94ee6e28ff5e76_***_Gary Tam
author Gary Tam
author2 Bailin Yang
Zhaoyi Jiang
Guoliang Wang
Gary Tam
Chao Song
Frederick W.B. Li
format Journal article
container_title Displays
container_start_page 102528
institution Swansea University
issn 0141-9382
doi_str_mv 10.1016/j.displa.2023.102528
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1016/j.displa.2023.102528
document_store_str 1
active_str 0
description Dynamic point clouds are widely used for 3D data representation in various applications such as immersive and mixed reality, robotics and autonomous driving. However, their irregularity and large scale make efficient compression and transmission a challenge. Existing methods require high bitrates to encode point clouds since temporal correlation is not well considered. This paper proposes an end-to-end dynamic point cloud compression network that operates in latent space, resulting in more accurate motion estimation and more effective motion compensation. Specifically, a multi-scale motion estimation network is introduced to obtain accurate motion vectors. Motion information computed at a coarser level is upsampled and warped to the finer level based on cost volume analysis for motion compensation. Additionally, a residual compression network is designed to mitigate the effects of noise and inaccurate predictions by encoding latent residuals, resulting in smaller conditional entropy and better results. The proposed method achieves an average 12.09% and 14.76% (D2) BD-Rate gain over state-of-the-art Deep Dynamic Point Cloud Compression (D-DPCC) in experimental results. Compared to V-PCC, our framework showed an average improvement of 81.29% (D1) and 77.57% (D2).
published_date 0001-01-01T16:47:01Z
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score 11.016235

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