A memory insensitive technique for large model simplification

P. Lindstrom, C. T. Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of Lindstrom 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.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Visualization Conference
EditorsT. Ertl, K. Joy, A. Varshney
Number of pages6
StatePublished - 2001
EventVisualization 2001 - San Diego, CA, United States
Duration: Oct 21 2001Oct 26 2001


OtherVisualization 2001
Country/TerritoryUnited States
CitySan Diego, CA


  • External sorting
  • Large data
  • Out-of-core algorithms
  • Polygonal surface simplification
  • Quadric error metrics

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering


Dive into the research topics of 'A memory insensitive technique for large model simplification'. Together they form a unique fingerprint.

Cite this