一种面向移动终端地理场景点云在线可视化的集成型索引
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摘要
针对现有点云索引研究方法欠考虑移动终端性能特点这一问题,提出了一种适用于移动端点云场景在线可视化的集成型空间索引。该索引首先利用考虑了移动终端网络带宽与计算渲染性能特点的改进型KD-tree实现点云数据的均衡划分与编码,在此基础上构建点云数据的LOD模型,并使用改进型八叉树管理其组织,最后通过改进型KD-tree的编码联结改进型八叉树形成〈1一级树:1二级树〉的优化型索引结构。该索引可支持移动端实现基于LOD的点云场景渲染策略,支持从数据块层面判断点云数据的空间关系,也支持数据的多线程查询。实验与分析表明:相比传统点云索引,该索引具有稳定的构建效率与优秀的空间查询性能,可为移动应用提供可靠的数据支持,能满足移动端点云在线可视化应用需求。
Current research on point clouds organization pays little attention to the performance characteristics of mobile devices.An integrated spatial index is proposed for online visualization of geo-scene point clouds on mobile devices.First,the index adopts an improved KD-tree which accounts for the network bandwidth and rendering performance of mobile terminal to implement a balanced division and coding of point clouds.Then,it builds a LOD model of point clouds and uses an improved Octree to organize the LOD models.Finally,the code of the improved KD-tree is concatenated with the improved Octree to form an optimized index structure of <1 level_1_tree:1 level_2_tree>.The proposed index supports LOD-based rendering,determination of spatial relations from data blocks and multi-threading data query.Experiments and analysis show that:compared to the traditional indexes of point clouds,this index has a stable construction efficiency and excellent performance of spatial query.It provides reliable data support for mobile applications and meets the diversified requirements of point clouds online visualization on mobile devices.
Current research on point clouds organization pays little attention to the performance characteristics of mobile devices.An integrated spatial index is proposed for online visualization of geo-scene point clouds on mobile devices.First,the index adopts an improved KD-tree which accounts for the network bandwidth and rendering performance of mobile terminal to implement a balanced division and coding of point clouds.Then,it builds a LOD model of point clouds and uses an improved Octree to organize the LOD models.Finally,the code of the improved KD-tree is concatenated with the improved Octree to form an optimized index structure of <1 level_1_tree:1 level_2_tree>.The proposed index supports LOD-based rendering,determination of spatial relations from data blocks and multi-threading data query.Experiments and analysis show that:compared to the traditional indexes of point clouds,this index has a stable construction efficiency and excellent performance of spatial query.It provides reliable data support for mobile applications and meets the diversified requirements of point clouds online visualization on mobile devices.
引文
[1]王雷.海量三维激光点云数据的组织与可视化研究[D].北京:北京工业大学,2016.WANG L.Research on Organization and Visualization of Massive 3D Laser Point Cloud Data[D].Beijing:Beijing University of Technology,2016.
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[9]陈茂霖,万幼川,田思忆,等.一种基于线性KD树的点云数据组织方法[J].测绘通报,2016(1):23-27.DOI:10.13474/j.cnki.11-2246.2016.0006CHEN M L,WAN Y C,TIAN S Y,et al.A method of organizing point clouds based on linear KD tree[J].Bulletin of Surveying and Mapping,2016(1):23-27.DOI:10.13474/j.cnki.11-2246.2016.0006
[10]路明月,何永健.三维海量点云数据的组织与索引方法[J].地球信息科学学报,2008,10(2):190-194.DOI:10.3969/j.issn.1560-8999.2008.02.012LU M Y,HE Y J.Organization and indexing method for 3D points cloud data[J].Geo-Information Science,2008,10(2):190-194.DOI:10.3969/j.issn.1560-8999.2008.02.012
[11]龚俊,柯胜男,朱庆,等.一种八叉树和三维R树集成的激光点云数据管理方法[J].测绘学报,2012,41(4):597-604.GONG J,KE S N,ZHU Q,et al.An efficient management method for point cloud data based on Octree and 3D R-tree[J].Acta Geodaetica et Cartographica Sinica,2012,41(4):597-604.
[12]YANG J,HUANG X.A hybrid spatial index for massive point cloud data management and visualization[J].Transactions in Gis,2015,18(S1):97-108.
[13]余飞,陈楚江,王丽园.海量激光雷达点云数据的多尺度可视化高效管理[J].工程勘察,2016,44(9):69-73.YU F,CHEN C J,WANG L Y.Multi-scale visualization and effective management of massive laser point cloud data[J].Geotechnical Investigation&Surveying,2016,44(9):69-73.
[14]张蕊,李广云,王力,等.车载LiDAR点云混合索引新方法[J].武汉大学学报(信息科学版):1-7[2018-03-11].https://doi.org/10.13203/j.whugis20160441.ZHANG R,LI G Y,WANG L,et al.A new method of hybrid index for mobile LIDAR point cloud data[J].Geomatics and Information Science of Wuhan University:1-7[2018-03-11].https://doi.org/10.13203/j.whugis20160441.
[15]PAMOS Má,SEGURA R.Point cloud visualization from large-range scanner in android devices[J].Spanish Computer Graphics Conference,2012:153-153.
[16]朱国强,甄海涛,李昕迪.基于iOS平台点数据显示的研究[J].自动化技术与应用,2015,34(5):24-26.ZHU G Q,ZHEN H T,LI X D.Research of point data show based on IOS platform[J].Computer Applications,2015,34(5):24-26.
[2]梁钰立.海量车载点云数据组织与快速可视化技术研究[D].北京:首都师范大学,2012.LIANG Y L.Research on Organization and Fast Visualization of Large Vehicle-Bore Point Cloud Data[D].Beijing:Capital Normal University,2012.
[3]MAENO T,DATE H,KANAI S.A data management method for efficient search and rendering of multiple large scale point clouds[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,XXXVIII-5/W12(5):203-208.DOI:10.11522/pscjspe.2011A.0.117.0
[4]MARTINEZ-RUBI O,VERHOEVEN S,MEERSBERGEN M V,et al.Taming the beast:Free and open-source massive point cloud web visualization[C]//Capturing Reality.Salzburg:The Survey Association,2015:23-25.
[5]SCHEIBLAUER C,WIMMER M.Out-of-core selection and editing of huge point clouds[J].Computers&Graphics,2011,35(2):342-351.DOI:10.1016/j.cag.2011.01.004
[6]WIMMER M,SCHEIBLAUER C.Instant points:Fast rendering of unprocessed point clouds[C]//Symposium on Point Based Graphics.Boston:DBLP,2006:129-136.DOI:10.2312/SPBG/SPBG06/129-136
[7]ELSEBERG J,BORRMANN D,NüCHTER A.One billion points in the cloud-An Octree for efficient processing of 3D laser scans[J].Isprs Journal of Photogrammetry&Remote Sensing,2013,76(1):76-88.DOI:10.1016/j.isprsjprs.2012.10.004
[8]徐鹏.海量三维点云数据的组织与可视化研究[D].南京:南京师范大学,2013.XU P.Massive Three-Dimensional Point Cloud Management and Visualization Research[D].Nanjing:Nanjing Normal University,2013.
[9]陈茂霖,万幼川,田思忆,等.一种基于线性KD树的点云数据组织方法[J].测绘通报,2016(1):23-27.DOI:10.13474/j.cnki.11-2246.2016.0006CHEN M L,WAN Y C,TIAN S Y,et al.A method of organizing point clouds based on linear KD tree[J].Bulletin of Surveying and Mapping,2016(1):23-27.DOI:10.13474/j.cnki.11-2246.2016.0006
[10]路明月,何永健.三维海量点云数据的组织与索引方法[J].地球信息科学学报,2008,10(2):190-194.DOI:10.3969/j.issn.1560-8999.2008.02.012LU M Y,HE Y J.Organization and indexing method for 3D points cloud data[J].Geo-Information Science,2008,10(2):190-194.DOI:10.3969/j.issn.1560-8999.2008.02.012
[11]龚俊,柯胜男,朱庆,等.一种八叉树和三维R树集成的激光点云数据管理方法[J].测绘学报,2012,41(4):597-604.GONG J,KE S N,ZHU Q,et al.An efficient management method for point cloud data based on Octree and 3D R-tree[J].Acta Geodaetica et Cartographica Sinica,2012,41(4):597-604.
[12]YANG J,HUANG X.A hybrid spatial index for massive point cloud data management and visualization[J].Transactions in Gis,2015,18(S1):97-108.
[13]余飞,陈楚江,王丽园.海量激光雷达点云数据的多尺度可视化高效管理[J].工程勘察,2016,44(9):69-73.YU F,CHEN C J,WANG L Y.Multi-scale visualization and effective management of massive laser point cloud data[J].Geotechnical Investigation&Surveying,2016,44(9):69-73.
[14]张蕊,李广云,王力,等.车载LiDAR点云混合索引新方法[J].武汉大学学报(信息科学版):1-7[2018-03-11].https://doi.org/10.13203/j.whugis20160441.ZHANG R,LI G Y,WANG L,et al.A new method of hybrid index for mobile LIDAR point cloud data[J].Geomatics and Information Science of Wuhan University:1-7[2018-03-11].https://doi.org/10.13203/j.whugis20160441.
[15]PAMOS Má,SEGURA R.Point cloud visualization from large-range scanner in android devices[J].Spanish Computer Graphics Conference,2012:153-153.
[16]朱国强,甄海涛,李昕迪.基于iOS平台点数据显示的研究[J].自动化技术与应用,2015,34(5):24-26.ZHU G Q,ZHEN H T,LI X D.Research of point data show based on IOS platform[J].Computer Applications,2015,34(5):24-26.
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