顾及自适应多细节层次的八叉树点云管理算法  

Management Algorithm of Point-Cloud Data Based on Octree Concerned with Adaptive Levels of Detail

作　　者：张俊峰[1,2] 许德合[1] 王小东[1] 

机构地区：[1]华北水利水电大学资源与环境学院,河南郑州450045 [2]成都理工大学国土资源部地学空间信息技术重点实验室,四川成都610059

出　　处：《西南交通大学学报》2016年第1期78-84,共7页Journal of Southwest Jiaotong University

基　　金：国土资源部地学空间信息技术重点实验室开放基金(KLGSIT2014-02);河南省教育厅科学技术研究重点项目(14A420001);地理信息工程国家测绘地理信息局重点实验室开放基金(201318)

摘　　要：为了解决大规模点云不易有效组织、动态可视化时冗余度大,且较难实现自适应显示的问题,提出顾及细节层次(levels of detail,LOD)的八叉树点云管理算法.该算法基于八叉树索引将扫描点限定在每个结点范围内,利用自上而下空间分割和自下而上参数计算相结合的预处理策略,减少实时阶段计算量,通过构建保守性模拟误差,使场景各处均可自动满足可视要求,并辅之以高效加速方法,实现了点云的有效组织和自适应流畅显示.实验研究表明,在优化的预处理和辅助加速策略支持下,与经典R树算法相比,该算法实时阶段计算量小,每帧自适应漫游平均时间在0.04 s以内.Large-scale point-cloud data are at dynamic visualization, and it is hard to not easy to organize effectively and have great redundancy realize the adaptive display. Aiming at these problems, a new algorithm concerned with the levels of detail （LOD） of point-cloud expression on the basis of octree structure was proposed. The algorithm assigned every scanning point into an octree node, and integrated top-down division with down-top calculation as the pretreatment strategy to reduce the amount of real-time calculation. Then it made any region meet the accuracy requirement and display speed automatically by building conservative simulation-error evaluation criteria. Furthermore, with the help of acceleration methods, large-scale point-cloud data could be organized effectively and expressed smoothly with little data redundancy. Preliminary experiments show that the algorithm has abilities to overcome the shortcoming of the classical R-tree methods; meanwhile, with the support of optimized pretreatment and assistant acceleration methods, the amount of real-time calculation is small and the time of each frame can hold within 0.04 s easily.

关 键 词：点云 八叉树 模拟误差 可见性裁剪 细节层次 

分 类 号：P208[天文地球—地图制图学与地理信息工程]