海量点云数据分布式并行处理技术综述  

Survey of Distributed Parallel Processing Technology for Massive Point Cloud Data

作　　者：宇超群[1] 门葆红[1] 王鑫[1] YU Chaoqun;MEN Baohong;WANG Xin(Information Engineering University. Zhengzhou 450001. China)

机构地区：[1]信息工程大学

出　　处：《信息工程大学学报》2018年第5期611-615,共5页Journal of Information Engineering University

基　　金：国家自然科学基金资助项目(41674042)

摘　　要：随着三维激光扫描技术的迅猛发展,测量过程中产生的点云数据量大幅增长,海量点云数据的高效处理遇到困难.针对海量点云数据的处理效率有待提高的问题,从大数据处理技术的角度,对近年点云处理领域出现的新技术进行探讨.首先分析点云数据的大数据特征,其次在分布式存储、并行计算技术层面,分析海量点云数据的研究现状,总结研究中达到的效果以及遇到的技术瓶颈,最后结合大数据技术以及点云数据处理的特点,对大数据背景下海量点云数据的处理提出展望.为海量点云数据的高效处理提供参考.With the rapid development of 3D laser scanning technology, the point cloud data pro- duced in the surveying increases significantly, and the efficient processing of large cloud points has encountered difficulties. To improve the massive point cloud data processing efficiency. the new technologies in point cloud data processing are discussed from the big data processing perspective. Firstly the big data characteristics of point cloud data are analyzed, then from the perspective of par- allel computing and distributed storage, the research status of the massive point cloud data is ana- lyzed, and the effect and the technical bottlenecks encountered in the study are summarized, finally, combined with the characteristics of big data technology and point cloud processing, the prospect of massive cloud data processing in the background of big data is put forward. The contents and views in the paper can provide references for efficient processing of massive point cloud data.

关 键 词：三维激光扫描 点云 大数据 并行计算 分布式存储 

分 类 号：P237[天文地球—摄影测量与遥感]