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Proceedings Paper

LOD-based clustering techniques for efficient large-scale terrain storage and visualization
Author(s): Xiaohong Bao; Renato Pajarola
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Paper Abstract

Large multi-resolution terrain data sets are usually stored out-of-core. To visualize terrain data at interactive frame rates, the data needs to be organized on disk, loaded into main memory part by part, then rendered efficiently. Many main-memory algorithms have been proposed for efficient vertex selection and mesh construction. Organization of terrain data on disk is quite difficult because the error, the triangulation dependency and the spatial location of each vertex all need to be considered. Previous terrain clustering algorithms did not consider the per-vertex approximation error of individual terrain data sets. Therefore, the vertex sequences on disk are exactly the same for any terrain. In this paper, we propose a novel clustering algorithm which introduces the level-of-detail (LOD) information to terrain data organization to map multi-resolution terrain data to external memory. In our approach the LOD parameters of the terrain elevation points are reflected during clustering. The experiments show that dynamic loading and paging of terrain data at varying LOD is very efficient and minimizes page faults. Additionally, the preprocessing of this algorithm is very fast and works from out-of-core.

Paper Details

Date Published: 9 June 2003
PDF: 11 pages
Proc. SPIE 5009, Visualization and Data Analysis 2003, (9 June 2003); doi: 10.1117/12.473936
Show Author Affiliations
Xiaohong Bao, Univ. of California/Irvine (United States)
Renato Pajarola, Univ. of California/Irvine (United States)

Published in SPIE Proceedings Vol. 5009:
Visualization and Data Analysis 2003
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Matti T. Groehn; Katy Boerner, Editor(s)

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