Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure

WANG Zhecheng; WAN Shuai; WEI Lei; YANG Fuzheng

doi:10.7652/xjtuxb202312002

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Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure

更新时间：2025-07-09

- Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure
- Vol. 57, Issue 12, Pages: 11-19(2023)
- 作者机构：
  
  1. 西北工业大学电子信息学院,西安,710129
  2. 皇家墨尔本理工大学工程学院, VIC,墨尔本,澳大利亚,3001
  3. 西安电子科技大学通信工程学院,西安,710071
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202312002
  CLC： TP391.4
- Online First：10 December 2023，
  
  Published：2023
- 稿件说明：
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WANG Zhecheng, WAN Shuai, WEI Lei, et al. Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure[J]. 2023, 57(12): 11-19.
DOI：

WANG Zhecheng, WAN Shuai, WEI Lei, et al. Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure[J]. 2023, 57(12): 11-19. DOI： 10.7652/xjtuxb202312002.

摘要

为进一步提高动态点云无损压缩的性能

提出了一种八叉树结构下的几何信息熵编码方法。针对帧内空间相关性

利用当前八叉树节点的已编解码邻域信息

建立帧内邻居节点上下文和帧内邻居父节点上下文。针对帧间时间相关性

将已编解码的上一帧点云作为参考帧

并将参考帧中与当前八叉树节点同位置的八叉树节点作为参考节点。使用参考节点及其父节点进行帧间上下文建模。为充分利用已建模的上下文

并准确地估计不同上下文下当前节点为非空的条件概率

提出了一种基于指数移动平均的二级概率估计方法:分别在帧内邻居节点上下文和帧内邻居父节点上下文下进行概率估计; 利用概率估计结果和帧间上下文建模新的二级上下文

并在二级上下文下再次进行概率估计; 采用二进制算术编码器实现无损压缩。选取常用的微软体素化人物上半身和8i体素化人物全身数据集进行性能测试

实验结果表明:与近年的方法相比

所提方法的无损压缩性能更高

平均编码增益达到2.2%～28.7%。

Abstract

To further improve the performance of lossless compression of dynamic point clouds

a method of geometry information entropy coding under an octree structure was proposed. By this method

for intra-frame spatial correlation

the contexts of intra-frame neighbor nodes and the contexts of intra-frame neighbor parent nodes are established based on the coded neighborhood information of the current octree node; for inter-frame temporal correlation

the previously coded point cloud frame is employed as the reference frame

and the octree node inside the reference frame at the same position as the current octree node is used as the reference node. The inter-frame context is modeled using the reference node and its parent node. To fully utilize the modeled contexts and accurately estimate the conditional probability that the current node is non-empty under different contexts

a second-level probability estimation method based on the exponential moving average was proposed. By this method

the probability is first estimated under the context of intra-frame neighbor nodes and the context of intra-frame neighbor parent nodes

respectively. Then

a set of second-level contexts are modeled using the inter-frame contexts and the results of the probability estimation

and the probability is estimated again under the second-level contexts. In the end

the lossless compression is realized by the binary arithmetic encoder. To evaluate the compression performance of the proposed method

the commonly used Microsoft voxelized upper bodies(MVUB)and 8i voxelized full bodies(8iVFB)datasets were selected for performance testing. The experimental results show that the proposed method has a higher lossless compression performance than the methods developed recently

with average coding gains of 2.2% to 28.7%.

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Keywords

references

李厚强, 李礼, 李竹. 点云编码综述 [J]. 中兴通讯技术, 2021, 27(1): 4-9.

LIHouqiang, LI Li, LI Zhu. A review of point cloud compression [J]. ZTE Technology Journal, 2021, 27(1): 4-9.

RUSU R B, COUSINS S. 3D is here: point cloud library(PCL)[C]//2011 IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2011: 1-4.

CAO Chao, PREDA M, ZAHARIA T. 3D point cloud compression: a survey [C]//24th International Conference on 3D Web Technology. New York, NY, USA: Association for Computing Machinery, 2019: 1-9.

张卉冉, 董震, 杨必胜, 等. 点云压缩研究进展与趋势 [J]. 武汉大学学报(信息科学版), 2023, 48(2): 192-205.

ZHANGHuiran, DONG Zhen, YANG Bisheng, et al. Progress and perspectives of point cloud compression [J]. Geomatics and Information Science of Wuhan University, 2023, 48(2): 192-205.

JACKINS C L, TANIMOTO S L. Oct-trees and their use in representing three-dimensional objects [J]. Computer Graphics and Image Processing, 1980, 14(3): 249-270.

GARCIA D C, DE QUEIROZ R L. Context-based octree coding for point-cloud video [C]//2017 IEEE International Conference on Image Processing(ICIP). Piscataway, NJ, USA: IEEE, 2017: 1412-1416.

DE QUEIROZ R L, GARCIA D C, CHOU P A, et al. Distance-based probability model for octree coding [J]. IEEE Signal Processing Letters, 2018, 25(6): 739-742.

GARCIA D C, DE QUEIROZ R L. Intra-frame context-based octree coding for point-cloud geometry [C]//2018 25th IEEE International Conference on Image Processing(ICIP). Piscataway, NJ, USA: IEEE, 2018: 1807-1811.

GARCIA D C, FONSECA T A, FERREIRA R U, et al. Geometry coding for dynamic voxelized point clouds using octrees and multiple contexts [J]. IEEE Transactions on Image Processing, 2020, 29: 313-322.

SCHWARZ S, PREDA M, BARONCINI V, et al. Emerging MPEG standards for point cloud compression [J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019, 9(1): 133-148.

ISO. Information technology: coded representation of immersive media: part 9 geometry-based point cloud compression: ISO/IEC23090-9:2023 [S]. Vernier, Geneva, Switzerland: ISO, 2023.

MARPE D, SCHWARZ H, WIEGAND T. Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(7): 620-636.

ALSHIN A, ALSHINA E, PARK J H. High precision probability estimation for CABAC [C]//2013 Visual Communications and Image Processing(VCIP). Piscataway, NJ, USA: IEEE, 2013: 1-6.

IM S K, CHAN K H. More probability estimators for CABAC in versatile video coding [C]//2020 IEEE 5th International Conference on Signal and Image Processing(ICSIP). Piscataway, NJ, USA: IEEE, 2020: 366-370.

KARWOWSKI D. Precise probability estimation of symbols in VVC CABAC entropy encoder [J]. IEEE Access, 2021, 9: 65361-65368.

SCHWARZ H, COBAN M, KARCZEWICZ M, et al. Quantization and entropy coding in the versatile video coding(VVC)standard [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(10): 3891-3906.

SAYOOD K. Introduction to data compression [M]. 4th ed. Waltham, MA, USA: Morgan Kaufmann, 2012.

PEIXOTO E. Intra-frame compression of point cloud geometry using dyadic decomposition [J]. IEEE Signal Processing Letters, 2020, 27: 246-250.

PEIXOTO E, MEDEIROS E, RAMALHO E. Silhouette 4d: an inter-frame lossless geometry coder of dynamic voxelized point clouds [C]//2020 IEEE International Conference on Image Processing(ICIP). Piscataway, NJ, USA: IEEE, 2020: 2691-2695.

RAMALHO E, PEIXOTO E, MEDEIROS E. Silhouette 4D with context selection: lossless geometry compression of dynamic point clouds [J]. IEEE Signal Processing Letters, 2021, 28: 1660-1664.

CAO Chao, PREDA M, ZAKHARCHENKO V, et al. Compression of sparse and dense dynamic point clouds: methods and standards [J]. Proceedings of the IEEE, 2021, 109(9): 1537-1558.

XIONG Jian, GAO Hao, WANG Miaohui, et al. Advanced geometry surface coding for dynamic point cloud compression [EB/OL].(2021-03-11)[2023-02-01]. https://arxiv.org/abs/2103.06549.

SHI Haoyu, LI Fan. Patch re-segmentation and packing for dynamic point cloud compression via back-and-forth structure [J]. IEEE Open Journal of Signal Processing, 2022, 3: 155-168.

HOLT C C. Forecasting seasonals and trends by exponentially weighted moving averages [J]. International Journal of Forecasting, 2004, 20(1): 5-10.

LOOP C, CAI Q, ESCOLANO S O, et al. Microsoft voxelized upper bodies: a voxelized point cloud dataset [EB/OL].(2016-05-25)[2023-02-01]. https://dms.mpeg.expert/doc_end_user/current_document.php?id=55507id_meeting=0.

D'EON E, HARRISON B, MYERS T, et al. 8i voxelized full bodies-a voxelized point cloud dataset [EB/OL].(2017-01-12)[2023-02-01]. https://dms.mpeg.expert/doc_end_user/current_document.php?id=57355id_meeting=0.

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