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3D Point Cloud Compression: A Survey

Authors:

Titus ZahariaAuthors Info & Claims

Web3D '19: Proceedings of the 24th International Conference on 3D Web Technology

Pages 1 - 9

https://doi.org/10.1145/3329714.3338130

Published: 26 July 2019 Publication History

Abstract

In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data. For a typical example of a point cloud with 0.7 million points per 3D frame at 30 fps, the point cloud raw video needs a bandwidth around 500MB/s. Thus, efficient compression methods are mandatory for ensuring the storage/transmission of such data, which include both geometry and attribute information. In the last years, the issue of 3D point cloud compression (3D-PCC) has emerged as a new field of research. In addition, an ISO/MPEG standardization process on 3D-PCC is currently on-going. In this paper, a comprehensive overview of the 3D-PCC state-of-the-art methods is proposed. Different families of approaches are identified, described in details and summarized, including 1D traversal compression, 2D-oriented techniques, which take leverage of existing 2D image/video compression technologies and finally purely 3D approaches, based on a direct analysis of the 3D data.

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https://doi.org/10.1109/TPAMI.2024.3462945
Wang JXue RLi JDing DLin YMa Z(2025)A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part I: GeometryIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.346293847:1(269-287)Online publication date: Jan-2025
https://doi.org/10.1109/TPAMI.2024.3462938
Adhuran JKhan NMartini M(2024)Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud CompressionSensors10.3390/s2405138224:5(1382)Online publication date: 21-Feb-2024
https://doi.org/10.3390/s24051382
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Published In

cover image ACM Conferences

Web3D '19: Proceedings of the 24th International Conference on 3D Web Technology

July 2019

131 pages

ISBN:9781450367981

DOI:10.1145/3329714

Conference Chairs:
Nicholas F. Polys
Virginia Tech., U.S.A
,
Mike McCann
Monterey Bay Aquarium Research Institute, U.S.A
,
Program Chairs:
Feng Liu
Mercer University, U.S.A
,
Andreas Plesch
Harvard University, U.S.A

Copyright © 2019 ACM.

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Published: 26 July 2019

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Web3D '19

Web3D '19: The 24th International Conference on 3D Web Technology

July 26 - 28, 2019

CA, LA, USA

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Cited By

Wang JXue RLi JDing DLin YMa Z(2025)A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part II: AttributeIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.346294547:1(252-268)Online publication date: Jan-2025
https://doi.org/10.1109/TPAMI.2024.3462945
Wang JXue RLi JDing DLin YMa Z(2025)A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part I: GeometryIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.346293847:1(269-287)Online publication date: Jan-2025
https://doi.org/10.1109/TPAMI.2024.3462938
Adhuran JKhan NMartini M(2024)Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud CompressionSensors10.3390/s2405138224:5(1382)Online publication date: 21-Feb-2024
https://doi.org/10.3390/s24051382
Ruiu PMascia LGrosso E(2024)Saliency-Guided Point Cloud Compression for 3D Live ReconstructionMultimodal Technologies and Interaction10.3390/mti80500368:5(36)Online publication date: 3-May-2024
https://doi.org/10.3390/mti8050036
Shao YSong FGao WLiu SLi G(2024)Texture-Guided Graph Transform Optimization for Point Cloud Attribute CompressionApplied Sciences10.3390/app1410409414:10(4094)Online publication date: 11-May-2024
https://doi.org/10.3390/app14104094
Qin TLi GGao WLiu S(2024)Multi-Grained Point Cloud Geometry Compression via Dual-Model Prediction with Extended OctreeACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367100120:9(1-30)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3671001
Zampieri ADelarue GBakr NAlliez P(2024)Entropy‐driven Progressive Compression of 3D Point CloudsComputer Graphics Forum10.1111/cgf.1513043:5Online publication date: 22-Aug-2024
https://doi.org/10.1111/cgf.15130
Huang RWang GZhang W(2024)Density-Adaptive Octree-Based Point Cloud Geometry Compression2024 International Conference on Ubiquitous Communication (Ucom)10.1109/Ucom62433.2024.10695889(227-231)Online publication date: 5-Jul-2024
https://doi.org/10.1109/Ucom62433.2024.10695889
Liu XWang JZhang W(2024)Fast Region-Adaptive Hierarchical Transform with Cross-Component Prediction and Coefficient Reordering2024 International Conference on Ubiquitous Communication (Ucom)10.1109/Ucom62433.2024.10695859(460-464)Online publication date: 5-Jul-2024
https://doi.org/10.1109/Ucom62433.2024.10695859
Yao KCao YZhang W(2024)Improved Trisoup Geometry Reconstruction in Geometry-Based Point Cloud Compression (G-PCC)2024 International Conference on Ubiquitous Communication (Ucom)10.1109/Ucom62433.2024.10695858(497-501)Online publication date: 5-Jul-2024
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