Research on mixed indexing model for cloud points

Author

Shi, Ruoming ; Qi, Xiaolong

Author_Institution

Sch. of Geomatics & Urban Inf., Beijing Univ. of Civil Eng. & Archit., Beijing, China

fYear

2012

fDate

22-27 July 2012

Firstpage

5301

Lastpage

5303

Abstract

About the three-dimensional cloud-points search, the two limitations of the searching efficiency and the visualization are analyzed. Firstly, the point cloud data that obtained by this system are dense and very huge. Secondly, if we just use one 3D index, we can not search the three-dimensional cloud-points effectively. A method is researched that knowledge of common 3D index and the characteristic of three-dimensional cloud-points is represented by object oriental technique and that the mixed indexing models are derived by inference, and then, the mixed indexing model of octree and R+ tree is built and used for three-dimensional cloud-points searching. Try to use the model to improve the efficiency of the three-dimensional cloud-points searching and visualization.

Keywords

data visualisation; geographic information systems; geophysical techniques; geophysics computing; information retrieval; object-oriented methods; octrees; 3D cloud-point search; 3D index; R+ tree; mixed indexing model; mixed indexing models; object oriental technique; point cloud data; searching efficiency; Data models; Indexing; Octrees; Solid modeling; Spatial databases; Spatial indexes;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Conference_Location

Munich

ISSN

2153-6996

Print_ISBN

978-1-4673-1160-1

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2012.6352412

Filename

6352412