The Visual Computer

, Volume 25, Issue 5–7, pp 411–421 | Cite as

Streaming surface sampling using Gaussian ε-nets

  • Pablo Diaz-Gutierrez
  • Jonas Bösch
  • Renato Pajarola
  • M. Gopi
Original Article

Abstract

We propose a robust, feature preserving and user-steerable mesh sampling algorithm, based on the one-to-many mapping of a regular sampling of the Gaussian sphere onto a given manifold surface. Most of the operations are local, and no global information is maintained. For this reason, our algorithm is amenable to a parallel or streaming implementation and is most suitable in situations when it is not possible to hold all the input data in memory at the same time. Using ε-nets, we analyze the sampling method and propose solutions to avoid shortcomings inherent to all localized sampling methods. Further, as a byproduct of our sampling algorithm, a shape approximation is produced. Finally, we demonstrate a streaming implementation that handles large meshes with a small memory footprint.

Keywords

Normal quantization Surface sampling Shape approximation Epsilon-nets 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Pablo Diaz-Gutierrez
    • 1
  • Jonas Bösch
    • 2
  • Renato Pajarola
    • 2
  • M. Gopi
    • 1
  1. 1.University of California, IrvineIrvineUSA
  2. 2.Department of InformaticsUniversity of ZürichZürichSwitzerland

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