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Laplacian Matrix Learning for Point Cloud Attribute Compression with Ternary Search-Based Adaptive Block Partition

Published: 28 October 2024 Publication History

Abstract

Graph Fourier Transform (GFT) has demonstrated significant effectiveness in point cloud attribute compression task. However, existing graph modeling methods are based on the geometric relationships of the points, which leads to reduced efficiency of graph transforms in cases where the correlation between attributes and geometry is weak. In this paper, we propose a novel graph modeling method based on attribute prediction values. Specifically, we utilize Gaussian priors to model prediction values, then use maximum a posteriori estimation to learn the Laplacian matrix that best fits the prediction values in order to conduct separate graph transforms on prediction values and ground truth values to derive residuals, and subsequently perform quantization and entropy coding on these residuals. Additionally, since the partitioning of point clouds directly affects the coding performance, We design an adaptive block partitioning method based on ternary search, which selects reference points using distance threshold r and performs block partitioning and non-reference point attribute prediction based on these reference points. By conducting ternary search on distance threshold r, we rapidly identify the optimal block partitioning strategy. Moreover, we introduce an efficient residual encoding method based on Morton codes for the attributes of reference points while the prediction attributes of non-reference points are modeled using the proposed graph-based modeling approach. Experimental results demonstrate that our method significantly outperforms two attribute compression methods employed by Moving Picture Experts Group (MPEG) in lossless geometry based attribute compression tasks, with an average of 30.57% BD-rate gain compared to Predictive Lifting Transform (PLT), and an average of 33.54% BD-rate gain compared to Region-Adaptive Hierarchical Transform (RAHT), which exhibits significantly improved rate-distortion performance over the current state-of-the-art method based on GFT.

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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)Open-Source Projects for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_9(255-272)Online publication date: 10-Oct-2024
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  1. Laplacian Matrix Learning for Point Cloud Attribute Compression with Ternary Search-Based Adaptive Block Partition

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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. adaptive block partition
    2. graph fourier transform
    3. laplacian matrix
    4. maximum a posteriori estimation.
    5. point cloud compression
    6. ternary search

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    • Research-article

    Funding Sources

    • Guangdong Basic and Applied Basic Research Foundation
    • Shenzhen Science and Technology Program
    • The Major Key Project of PCL
    • Natural Science Foundation of China
    • Sponsored by CAAI-MindSpore Open Fund, developed on OpenI Community
    • Guangdong Province Pearl River Talent 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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    • (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)Open-Source Projects for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_9(255-272)Online publication date: 10-Oct-2024
    • (2024)Point Cloud-Language Multi-modal LearningDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_8(227-254)Online publication date: 10-Oct-2024
    • (2024)Point Cloud Pre-trained Models and Large ModelsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_7(195-225)Online publication date: 10-Oct-2024
    • (2024)Deep-Learning-Based Point Cloud Analysis IIDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_6(163-193)Online publication date: 10-Oct-2024
    • (2024)Deep-Learning-Based Point Cloud Analysis IDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_5(131-162)Online publication date: 10-Oct-2024
    • (2024)Deep-Learning-Based Point Cloud Enhancement IIDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_4(99-130)Online publication date: 10-Oct-2024
    • (2024)Deep-Learning-based Point Cloud Enhancement IDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_3(71-97)Online publication date: 10-Oct-2024
    • (2024)Learning Basics for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_2(29-70)Online publication date: 10-Oct-2024
    • (2024)Future Work on Deep Learning-Based Point Cloud TechnologiesDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_11(301-315)Online publication date: 10-Oct-2024
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