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OpenPointCloud: An Open-Source Algorithm Library of Deep Learning Based Point Cloud Compression

Published: 10 October 2022 Publication History

Abstract

This paper gives an overview of OpenPointCloud, the first open-source algorithm library containing outstanding deep learning methods on point cloud compression (PCC). We provide an introduction of our implementations, including 8 methods on lossless geometry PCC and lossy geometry PCC. Principles and contributions of these methods in our algorithm library are illustrated, which are also implemented with different deep learning programming frameworks, such as TensorFlow, Pytorch and TensorLayer. In order to systematically evaluate the performances of all these methods, we conduct a comprehensive benchmarking test. We provide analyses and comparisons of their performances according to their categories and draw constructive conclusions. This algorithm library has been released at https://git.openi.org.cn/OpenPointCloud.

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Presentation video of OpenPointCloud, An Open-Source Algorithm Library of Deep Learning Based Point Cloud Compression.

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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)OpenDIC: An Open-Source Library and Performance Evaluation for Deep-learning-based Image CompressionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685519(11202-11205)Online publication date: 28-Oct-2024
  • (2024)PCHMVision: An Open-Source Library of Point Cloud Compression for Human and Machine VisionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685513(11239-11243)Online publication date: 28-Oct-2024
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cover image ACM Conferences
MM '22: Proceedings of the 30th ACM International Conference on Multimedia
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
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 ACM 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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Publication History

Published: 10 October 2022

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Author Tags

  1. algorithm library
  2. deep learning
  3. geometry compression
  4. open-source software
  5. point cloud

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  • Short-paper

Funding Sources

  • Natural Science Foundation of China
  • Guangdong Basic and Applied Basic Research Foundation
  • National Key R&D Program of China
  • Shenzhen Fundamental Research Program
  • The Major Key Project of PCL
  • Shenzhen Science and Technology Plan Basic Research Project

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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)OpenDIC: An Open-Source Library and Performance Evaluation for Deep-learning-based Image CompressionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685519(11202-11205)Online publication date: 28-Oct-2024
  • (2024)PCHMVision: An Open-Source Library of Point Cloud Compression for Human and Machine VisionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685513(11239-11243)Online publication date: 28-Oct-2024
  • (2024)LearningPCC: A PyTorch Library for Learning-Based Point Cloud CompressionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685512(11234-11238)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
  • (2024)ViewPCGC: View-Guided Learned Point Cloud Geometry CompressionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681225(7152-7161)Online publication date: 28-Oct-2024
  • (2024)SPCGC: Scalable Point Cloud Geometry Compression for Machine Vision2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610894(17272-17278)Online publication date: 13-May-2024
  • (2024)Adaptive Intra Period Size for Deep Learning-Based Screen Content Video Coding2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW63481.2024.10645479(1-6)Online publication date: 15-Jul-2024
  • (2024)Variable-Rate Point Cloud Geometry Compression Based on Feature Adjustment and Interpolation2024 Data Compression Conference (DCC)10.1109/DCC58796.2024.00014(63-72)Online publication date: 19-Mar-2024
  • (2024)Interpretable Task-inspired Adaptive Filter Pruning for Neural Networks Under Multiple ConstraintsInternational Journal of Computer Vision10.1007/s11263-023-01972-x132:6(2060-2076)Online publication date: 6-Jan-2024
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