The Visual Computer

, Volume 23, Issue 6, pp 381–395 | Cite as

On stochastic methods for surface reconstruction

  • Waqar Saleem
  • Oliver Schall
  • Giuseppe Patanè
  • Alexander Belyaev
  • Hans-Peter Seidel
Original Article

Abstract

In this article, we present and discuss three statistical methods for surface reconstruction. A typical input to a surface reconstruction technique consists of a large set of points that has been sampled from a smooth surface and contains uncertain data in the form of noise and outliers. We first present a method that filters out uncertain and redundant information yielding a more accurate and economical surface representation. Then we present two methods, each of which converts the input point data to a standard shape representation; the first produces an implicit representation while the second yields a triangle mesh.

Keywords

Surface reconstruction Point cloud denoising Sparse implicits Statistical learning 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Waqar Saleem
    • 1
  • Oliver Schall
    • 1
  • Giuseppe Patanè
    • 2
  • Alexander Belyaev
    • 1
  • Hans-Peter Seidel
    • 1
  1. 1.Max-Planck-Institut Informatik (MPII)SaarbrückenGermany
  2. 2.IMATI-GE CNRGenovaItaly

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