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PCHMVision: An Open-Source Library of Point Cloud Compression for Human and Machine Vision

Published: 28 October 2024 Publication History

Abstract

In today's era, three-dimensional point cloud data is not only voluminous but also widely applicable. Therefore, data compression has become a crucial step prior to processing. Although existing 3D point cloud compression techniques primarily focus on fidelity, in practical applications, the vast majority of compressed data serves machine perception tasks. Therefore, point cloud compression tailored for machine perception becomes particularly significant. To address this problem, we introduce an innovative point cloud compression algorithm library specifically designed for both machine and human perceptual requirements. This library represents the first collection of multi-perception point cloud compression algorithms on the PyTorch platform, integrating eleven advanced, learning-based algorithms. We category and analyze these algorithms in depth, according to different analysis tasks, to facilitate a better understanding and comparison. Moreover, we successfully replicate these algorithms and meticulously organize the pre-processing of point cloud data and the analysis networks for downstream tasks. Ultimately, we conduct experiments on multiple perceptual datasets for compression and analysis tasks, with results comprehensively summarized across various performance metrics. We will continue to update these algorithms to ease their adoption by researchers.

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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)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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Publication History

Published: 28 October 2024

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Author Tags

  1. human and machine vision.
  2. point cloud compression

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  • Research-article

Funding Sources

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

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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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