Spatio-Temporal Context-Guided Algorithm for Lossless Point Cloud Geometry Compression  

作　　者：ZHANG Huiran DONG Zhen WANG Mingsheng 

机构地区：[1]Guangzhou Urban Planning and Design Survey Research Institute,Guangzhou 510060,China [2]Guangdong Enterprise Key Laboratory for Urban Sensing,Monitoring and Early Warning,Guangzhou 510060,China [3]State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China

出　　处：《ZTE Communications》2023年第4期17-28,共12页中兴通讯技术（英文版）

摘　　要：Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence,autonomous driving,and cultural heritage preservation.However,point cloud data are distributed irregularly and discontinuously in spatial and temporal domains,where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem.In this paper,we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression.The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis.Then,it introduces a prediction method where both intraframe and inter-frame point clouds are available,by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm.Finally,the few prediction residual is efficiently compressed with optimal context-guided and adaptive fastmode arithmetic coding techniques.Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information,and is suitable for 3D point cloud compression applicable to various types of scenes.

关 键 词：point cloud geometry compression single-frame point clouds multi-frame point clouds predictive coding arithmetic coding 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]