On stochastic methods for surface reconstruction

  • Authors:
  • Waqar Saleem;Oliver Schall;Giuseppe Patanè;Alexander Belyaev;Hans-Peter Seidel

  • Affiliations:
  • Max-Planck-Institut Informatik (MPII), Saarbrücken, Germany;Max-Planck-Institut Informatik (MPII), Saarbrücken, Germany;IMATI-GE CNR, Genova, Italy;Max-Planck-Institut Informatik (MPII), Saarbrücken, Germany;Max-Planck-Institut Informatik (MPII), Saarbrücken, Germany

  • Venue:
  • The Visual Computer: International Journal of Computer Graphics
  • Year:
  • 2007

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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.