Volume 42 Issue 8
Aug.  2017
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YAN Li, HU Xiaobin, XIE Hong. Data Management and Visualization of Mobile Laser Scanning Point Cloud[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1131-1136. DOI: 10.13203/j.whugis20150386
Citation: YAN Li, HU Xiaobin, XIE Hong. Data Management and Visualization of Mobile Laser Scanning Point Cloud[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1131-1136. DOI: 10.13203/j.whugis20150386
shu

Data Management and Visualization of Mobile Laser Scanning Point Cloud

  • 1.

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Funds: 

The National Public Welfare Foundation for Surveying, Mapping and Geoinformation Industry Research of China with Project 201512008

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  • Author Bio:

    YAN Li, PhD, professor, specializes photogrammetry, remote sensing imagery processing and application, 3D laser scanning imaging measurement technology. E-mail:lyan@sgg.whu.edu.cn

  • Corresponding author:

    HU Xiaobin, PhD, E-mail: 494278321@qq.com

  • Received Date: June 21, 2015
  • Published Date: August 04, 2017
    Graphical Abstract
    • Abstract
  • This paper proposed an organization method of laser point cloud data for the efficient management and rapid visualization of the massive point cloud data of vehicle-mounted laser scanner. In this paper, both the original point cloud data and its trajectory are sectioned for the fast indexing firstly, then the LOD(levels of details) index of each section is build based on octree structure. With a tile type and multiresolution storage mode based on folder system, the depth of octree structure is represented by the level of folder directory, and in every node folder, the corresponding point cloud data file and its node properties file are both include. The storage method of this paper decreases the preprocessing time greatly and can support concurrent access owing to mutually independence of each node. Moreover, with the application of view frustum culling technology and multi-thread dynamic dispatch technology, the rendering and roaming of massive point cloud can realize real time updating according to viewpoint change, which significantly improves the dispatch efficiency of vehicle-mounted laser point cloud data. In the experiment, our method was compared with several popular software of point cloud data processing (e.g. XGRT, Quick Terrain Reader) on the data dispatch. The results show our method has obvious advantage on storage capacity, data access time, memory footprint, rendering frame rate and so on, it indicates our method is effective for the management of vehicle-mounted laser point cloud data and can improve the production efficiency for indoor-work of vehicle-mounted laser point cloud data processing.
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    • References (18)
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