A memory insensitive technique for large model simplification

被引:42
作者
Lindstrom, P [1 ]
Silva, CT [1 ]
机构
[1] Lawrence Livermore Natl Lab, Ctr Appl Sci Comp, Livermore, CA 94551 USA
来源
VISUALIZATION 2001, PROCEEDINGS | 2001年
关键词
polygonal surface simplification; large data; out-of-core algorithms; external sorting; quadric error metrics;
D O I
10.1109/VISUAL.2001.964502
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of Lindstrom [20] which increase the quality of approximations and extend the applicability of the algorithm to an even larger class of compute systems. The original OoCS algorithm has memory complexity that depends on the size of the output mesh. but no dependency on the size of the input mesh, That is, it can be used to simplify meshes of arbitrarily large size, but the complexity of the output mesh is limited by the amount of memory available. Our first contribution is a version of OoCS that removes the dependency of having enough memory to hold (even) the simplified mesh. With our new algorithm. the whole process is made essentially independent of the available memory on the host computer. Our new technique uses disk instead of main memory, but it is carefully designed to avoid costly random accesses. Our two other contributions improve the quality of the approximations generated by OoCS. We propose a scheme for preserving surface boundaries which does not use connectivity information, and a scheme for constraining the position of the "representative vertex" of a grid cell to an optimal position inside the cell.
引用
收藏
页码:121 / 126
页数:6
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