research-article

Using neighbouring nodes for the compression of octrees representing the geometry of point clouds

Authors:

Sébastien Lasserre,

Shouxing QuAuthors Info & Claims

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

Pages 145 - 153

https://doi.org/10.1145/3304109.3306224

Published: 18 June 2019 Publication History

Abstract

The geometry of a point cloud is commonly represented by an octree recursively decomposing a 3D volume into eight child sub-volumes. Said volumes and sub-volumes are associated with nodes and child-nodes of the octree. The geometry is defined by the occupancy information indicating the presence or not of a point in each of the sub-volumes. This naturally leads to an eight-bit occupancy information to be coded for each internal node of the tree.

This paper introduces a new binarization scheme to efficiently compress the occupancy information using an optimal set of binary entropy coders. Then, it is shown how using the occupancy information of neighbouring nodes helps to compress the occupancy bits associated with the child nodes of the current node. This information is used to contextualise the binarization scheme by computing, firstly a neighbour configuration, secondly a number of neighbours with occupied child nodes adjacent to the current child node, and thirdly an intra predictor.

Objective results show lossless geometry compression gains between 60% and 75% on virtual reality oriented dense point clouds used by MPEG, reaching sub-bit per point bit-rates for the lossless intra coding of such point clouds. Solid gains (between 5% and 25% depending upon the sampling) are also observed on sparse point clouds captured by a LiDAR (Light Detection and Ranging) device attached to a moving vehicle or representing 3D maps.

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Digital Library

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Cited By

Hoang DBhatia HLindstrom PPascucci V(2024)Progressive Tree-Based Compression of Large-Scale Particle DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.326062830:7(4321-4338)Online publication date: Jul-2024
https://doi.org/10.1109/TVCG.2023.3260628
Sun CYuan HMao XLu XHamzaoui R(2024)Enhancing Octree-Based Context Models for Point Cloud Geometry Compression With Attention-Based Child Node Number PredictionIEEE Signal Processing Letters10.1109/LSP.2024.342691831(1835-1839)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3426918
Sun CYuan HLi SLu XHamzaoui R(2024)Enhancing Context Models for Point Cloud Geometry Compression With Context Feature Residuals and Multi-LossIEEE Journal on Emerging and Selected Topics in Circuits and Systems10.1109/JETCAS.2024.336772914:2(224-234)Online publication date: Jun-2024
https://doi.org/10.1109/JETCAS.2024.3367729
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Index Terms

Using neighbouring nodes for the compression of octrees representing the geometry of point clouds
1. Mathematics of computing
  1. Information theory
    1. Coding theory
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Data compression

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Published In

cover image ACM Conferences

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

June 2019

374 pages

ISBN:9781450362979

DOI:10.1145/3304109

General Chair:
Michael Zink
University of Massachusetts Amherst
,
Program Chairs:
Laura Toni
University College London
,
Ali C. Begen
Özyeğin University/Networked Media

Copyright © 2019 ACM.

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: 18 June 2019

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MMSys '19: 10th ACM Multimedia Systems Conference

June 18 - 21, 2019

Massachusetts, Amherst

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MMSys '19 Paper Acceptance Rate 40 of 82 submissions, 49%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Hoang DBhatia HLindstrom PPascucci V(2024)Progressive Tree-Based Compression of Large-Scale Particle DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.326062830:7(4321-4338)Online publication date: Jul-2024
https://doi.org/10.1109/TVCG.2023.3260628
Sun CYuan HMao XLu XHamzaoui R(2024)Enhancing Octree-Based Context Models for Point Cloud Geometry Compression With Attention-Based Child Node Number PredictionIEEE Signal Processing Letters10.1109/LSP.2024.342691831(1835-1839)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3426918
Sun CYuan HLi SLu XHamzaoui R(2024)Enhancing Context Models for Point Cloud Geometry Compression With Context Feature Residuals and Multi-LossIEEE Journal on Emerging and Selected Topics in Circuits and Systems10.1109/JETCAS.2024.336772914:2(224-234)Online publication date: Jun-2024
https://doi.org/10.1109/JETCAS.2024.3367729
Hao SWan STian TZhang WYang F(2024)Improving Optimal Binarization with Update On-the-fly in G-PCC Entropy Coding: Probability Initialization and Adaptive Bounds Setting for Context Models2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558219(1-5)Online publication date: 19-May-2024
https://doi.org/10.1109/ISCAS58744.2024.10558219
Lin TLin CChiou YChen S(2024)Improved grid refine segmentation for 3D point cloud in video-based point cloud compression (V-PCC)Multimedia Tools and Applications10.1007/s11042-023-17845-x83:23(62701-62720)Online publication date: 8-Jan-2024
https://doi.org/10.1007/s11042-023-17845-x
Feng YHansen PWhatmough PLu GZhu Y(2023)Fast and Accurate: Video Enhancement Using Sparse Depth2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00447(4481-4489)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00447
Shao YLi GZhang QGao WLiu S(2023)Nonrigid Registration-Based Progressive Motion Compensation for Point Cloud Geometry CompressionIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.332128961(1-14)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3321289
Ali MLiu L(2023)Light-Weight Pointcloud Representation with Sparse Gaussian Process2023 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA48891.2023.10161111(4931-4937)Online publication date: 29-May-2023
https://doi.org/10.1109/ICRA48891.2023.10161111
Martens JBlankenbach J(2023)A decomposition scheme for continuous Level of Detail, streaming and lossy compression of unordered point cloudsGraphical Models10.1016/j.gmod.2023.101208130:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.gmod.2023.101208
Wang ZWan SWei L(2023)Optimized octree codec for geometry-based point cloud compressionSignal, Image and Video Processing10.1007/s11760-023-02803-918:1(761-772)Online publication date: 14-Oct-2023
https://doi.org/10.1007/s11760-023-02803-9
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