skip to main content
10.1145/3647649.3647693acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicigpConference Proceedingsconference-collections
research-article

Point Cloud Attribute Compression based on Adaptive Sampling and Quantization

Published: 03 May 2024 Publication History

Abstract

With the rapid development of 3D sensing technology, point cloud compression has become a research hotspot in the field of multimedia. Geometry Based Point Cloud Compression (G-PCC) developed by MPEG 3DG is one of the most important frameworks for point cloud compression. Recently, the Level of Detail (LOD) method for attribute data coding in G-PCC has received a lot of attention. In the prediction transformation and lifting transformation methods of G-PCC, the fixed sampling distance is used to divide the level of detail, and the same quantization step is used for the same level. However, point clouds usually have different texture complexity in different local regions. In this paper, we propose an adaptive sampling and quantization method based on the texture complexity of point cloud to improve the attribute compression performance. Experimental results show that the proposed method can achieve better coding performance with maintaining more detail information compared with that of the MPEG G-PCC reference software.

References

[1]
X Wang, Y Mizukami, M Tada, and F Matsuno. Navigation of a mobile robot in a dynamic environment using a point cloud map[J]. Artificial Life and Robotics, 2021, 26: 10-20.
[2]
A. P. Placitelli and L. Gallo. Low-Cost Augmented Reality Systems via 3D Point Cloud Sensors. 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, Dijon, France, 2011, pp. 188-192.
[3]
Li Y, Ma L, Zhong Z, Liu F, Michael A, Cao D, Li J. Deep learning for lidar point clouds in autonomous driving: A review[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(8): 3412-3432.
[4]
Linh Tao, Trung Nguyen, Tinh Nguyen, Toshio Ito, and Tam Bui, "An Adaptive Differential Evolution Algorithm with a Point-Based Approach for 3D Point Cloud Registration," Journal of Image and Graphics, Vol. 10, No. 1, pp. 1-9, March 2022.
[5]
Wang H, Huang Y, Zhang G, Rong Y. A novel method for dense point cloud reconstruction and weld seam detection for tubesheet welding robot. Optics & Laser Technology. 2023 Aug 1;163:109346.
[6]
MPEG 3DG. G-PCC codec description, document w20351[R]. Online: ISO/IEC JTC1/SC29/WG07 MPEG, 2021.
[7]
Geiger A, Lenz P, Stiller C, and Urtasun R. Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research. 2013;32(11):1231-1237.
[8]
MPEG 3DG, “MPEG database,” https://mpegfs.int-evry.fr/ws-mpegcontent/MPEG-I/Part05-PointCloudCompression/, Accessed: Feb.2022.
[9]
Zhang C, Florencio D, and Loop C. Point cloud attribute compression with graph transform[C]//2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014: 2066-2070.
[10]
Y. Shao, Z. Zhang, Z. Li, K. Fan and G. Li. Attribute compression of 3D point clouds using Laplacian sparsity optimized graph transform. 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA, 2017, pp. 1-4.
[11]
Sandri G, De Queiroz R L, and Chou P A . Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform[J]. IEEE Transactions on Image Processing, 2018, 25(8):3947-3956.
[12]
Wei L, Wan S, Wang Z, Ding X B, and Zhang W. Optimization Method for Level of Detail of Lossless Point Cloud Compression[J]. Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2021, 55(9): 88-96.
[13]
J. Lu, W. Zhang, L. Yang and F. Yang, "Distribution-Driven Predictor Screening For Point Cloud Attribute Compression," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 2966-2970.
[14]
MPEG 3DG Group. A Progressive Quantization for LoD-based attribute Predicting Transform coding[R]. OnLine: ISO/IEC JTC1/SC29/WG7 input document m55859.
[15]
Graziosi D, Nakagami O, Kuma S, Zaghetto A, Suzuki T, and Tabatabai A. An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)[J]. APSIPA Transactions on Signal and Information Processing, 2020, 9.
[16]
MPEG 3DG Group. Using L1 norm for nearest neighbour search in Prediction and Lifting schemes, ISO/IEC JTC1/SC29/WG11 input document m51011.
[17]
S. Barburiceanu, R. Terebes and S. Meza, "Improved 3D Co-Occurrence Matrix for Texture Description and Classification," 2020 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2020, pp. 1-4.
[18]
MPEG 3DG, “MPEG-pcc-tmc13-v14.0,” http://mpegx.int-evry.fr/software/MPEG/PCC/TM/mpeg-pcc-tmc13/-/tree/release-v14.0/, Accessed: Feb.2022.
[19]
MPEG 3DG, “Common test conditions for point cloud compression, document N19324,” ISO/IEC JTC 1/SC 29/WE 11 MPEG, Alpbach, Apr. 2020.
[20]
G. Bjøntegaard, Improvements of the BD-PSNR model, Standard VCEG-AI11, Berlin, Germany, Jul. 2008.
[21]
Q. Yin, Q. Ren, L. Zhao, W. Wang and J. Chen, "Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction," 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Chengdu, China, 2021, pp. 1-5.

Index Terms

  1. Point Cloud Attribute Compression based on Adaptive Sampling and Quantization

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing
    January 2024
    480 pages
    ISBN:9798400716720
    DOI:10.1145/3647649
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 May 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • Project of Education Department of Jilin Province
    • Doctoral Startup Fund of Yanbian University

    Conference

    ICIGP 2024

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 27
      Total Downloads
    • Downloads (Last 12 months)27
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media