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LearningPCC: A PyTorch Library for Learning-Based Point Cloud Compression

Published: 28 October 2024 Publication History

Abstract

Three-dimensional point cloud data is one of the most extensively used data representation today, favored in various fields for its realistic and lifelike visual effects. However, the substantial volume of data poses significant challenges for storage and transmission. To advance point cloud compression (PCC) technology, we develop a learning-based PCC algorithm library, namely LearningPCC. To our knowledge, this is the first comprehensive set of algorithms that is compatible with all types of point cloud data. This PyTorch library incorporates eleven learning-based algorithms that address both geometry and attribute compression of point cloud data. We categorize the existing methods into six main classes and thoroughly introduce and analyze the principles of these algorithms. Moreover, we conduct performance evaluations using point clouds with various densities, offering detailed test results on several compression metrics, such as RD curves, BD-BR gains, compression ratio improvements, and encoding times. We will provide researchers with convenient access to these methods, replicate codes, and experiment results. Our commitment includes maintaining and updating these algorithms to offer researchers the latest in compression technologies.

References

[1]
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. 2015. Shapenet: An Information-rich 3D Model Repository. ArXiv Preprint ArXiv:1512.03012 (2015).
[2]
Chunyang Fu, Ge Li, Rui Song, Wei Gao, and Shan Liu. 2022. OctAttention: Octree-based Large-scale Contexts Model for Point Cloud Compression. ArXiv Preprint ArXiv:2202.06028 (2022).
[3]
Wei Gao, Hua Ye, Huiming Zheng, Yuyang Wu, and Liang Xie. 2022. OpenPointCloud: An Open-Source Algorithm Library of Deep Learning Based Point Cloud Compression. In ACM International Conference on Multimedia. 7347--7350.
[4]
Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3354--3361. https://doi.org/10.1109/CVPR.2012.6248074
[5]
AVS Point Cloud Compression Working Group. September, 2022. Reference Software Algorithm Description of AVS Point Cloud Coding. Audio Video Standard, N3445, Online ( September, 2022).
[6]
Emre Can Kaya and Ioan Tabus. 2021. Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds. In International Workshop on Multimedia Signal Processing. IEEE, 1--6.
[7]
Khaled Mammou, Philip A. Chou, David Flynn, Maja Krivoku'a, Ohji Nakagami, and Toshiyasu Sugio. January, 2019. G-PCC Codec Description v2. ISO/IEC TC1/SC29/WG11 MPEG, N18189, Marrakech ( January, 2019).
[8]
Dat Thanh Nguyen and André Kaup. 2023. Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model. IEEE Transactions on Circuits and Systems for Video Technology (2023).
[9]
Dat Thanh Nguyen, Maurice Quach, and Giuseppe Valenzise. 2021. Learning-based Lossless Compression of 3D Point Cloud Geometry. In IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 4220--4224.
[10]
Jiahao Pang, Muhammad Asad Lodhi, and Dong Tian. 2022. GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression. In International Workshop on Advances in Point Cloud Compression, Processing and Analysis. 11--19.
[11]
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE conference on computer vision and pattern recognition. 652--660.
[12]
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017. Pointnet: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Advances in Neural Information Processing Systems, Vol. 30 (2017).
[13]
Maurice Quach, Giuseppe Valenzise, and Frederic Dufaux. 2020. Folding-based Compression of Point Cloud Attributes. In IEEE International Conference on Image Processing. IEEE, 3309--3313.
[14]
Rui Song, Chunyang Fu, Shan Liu, and Ge Li. 2023. Efficient Hierarchical Entropy Model for Learned Point Cloud Compression. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14368--14377.
[15]
Jianqiang Wang, Dandan Ding, Zhu Li, Xiaoxing Feng, Chuntong Cao, and Zhan Ma. 2021. Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression. ArXiv Preprint ArXiv:2111.10633 (2021).
[16]
Jianqiang Wang, Dandan Ding, Zhu Li, and Zhan Ma. 2021. Multiscale Point Cloud Geometry Compression. In Data Compression Conference. IEEE, 73--82.
[17]
Jianqiang Wang and Zhan Ma. 2022. Sparse Tensor-based Point Cloud Attribute Compression. In International Conference on Multimedia Information Processing and Retrieval. IEEE, 59--64.
[18]
Yuyang Wu, Liang Xie, Shangkun Sun, Wei Gao, and Yiqiang Yan. 2024. Adaptive Intra Period Size for Deep Learning-based Screen Content Video Coding. In IEEE International Conference on Multimedia and Expo Workshops. IEEE.
[19]
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, and Xiaoou Tang. 2015. 3D ShapeNets: A deep representation for volumetric shapes. In IEEE Conference on Computer Vision and Pattern Recognition. 1912--1920.
[20]
Liang Xie, Wei Gao, and Songlin Fan. 2024. PDNet: Parallel Dual-branch Network for Point Cloud Geometry Compression and Analysis. In 2024 Data Compression Conference. 596--596. https://doi.org/10.1109/DCC58796.2024.00113
[21]
Liang Xie, Wei Gao, and Huiming Zheng. 2022. End-to-end Point Cloud Geometry Compression and Analysis with Sparse Tensor. In International Workshop on Advances in Point Cloud Compression, Processing and Analysis. 27--32.
[22]
Liang Xie, Wei Gao, Huiming Zheng, and Ge Li. 2024. SPCGC: Scalable Point Cloud Geometry Compression for Machine Vision. In IEEE International Conference on Robotics and Automation. 594--595.
[23]
Liang Xie, Wei Gao, Huiming Zheng, and Hua Ye. 2024. Semantic-Aware Visual Decomposition for Point Cloud Geometry Compression. In 2024 Data Compression Conference. 595--595. https://doi.org/10.1109/DCC58796.2024.00112
[24]
Vladyslav Zakharchenko. January, 2019. V-PCC Codec Description. ISO/IEC TC1/SC29/WG11 MPEG, N18190, Marrakech ( January, 2019).
[25]
Junteng Zhang, Tong Chen, Dandan Ding, and Zhan Ma. 2023. YOGA: Yet Another Geometry-based Point Cloud Compressor. In ACM International Conference on Multimedia. 9070--9081.

Cited By

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  • (2024)Point Cloud Compression, Enhancement and Applications: From 3D Perception to Large ModelsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3689172(11292-11293)Online publication date: 28-Oct-2024
  • (2024)ROI-Guided Point Cloud Geometry Compression Towards Human and Machine VisionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681301(3741-3750)Online publication date: 28-Oct-2024

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cover image ACM Conferences
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
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].

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New York, NY, United States

Publication History

Published: 28 October 2024

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Author Tags

  1. open-source software.
  2. point cloud compression

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  • Research-article

Funding Sources

  • The Major Key Project of PCL
  • Guangdong Basic and Applied Basic Research Foundation
  • Natural Science Foundation of China
  • Guangdong Province Pearl River Talent Program
  • Shenzhen Science and Technology Program
  • CAAI-MindSpore Open Fund, developed on OpenI Community

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MM '24
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MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia

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MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2024)Point Cloud Compression, Enhancement and Applications: From 3D Perception to Large ModelsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3689172(11292-11293)Online publication date: 28-Oct-2024
  • (2024)ROI-Guided Point Cloud Geometry Compression Towards Human and Machine VisionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681301(3741-3750)Online publication date: 28-Oct-2024

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