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On stochastic methods for surface reconstruction

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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.

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Authors and Affiliations

  1. Max-Planck-Institut Informatik (MPII), Saarbrücken, Germany

    Waqar Saleem, Oliver Schall, Alexander Belyaev & Hans-Peter Seidel

  2. IMATI-GE CNR, Genova, Italy

    Giuseppe Patanè

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  1. Waqar Saleem
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  2. Oliver Schall
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  3. Giuseppe Patanè
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Saleem, W., Schall, O., Patanè, G. et al. On stochastic methods for surface reconstruction. Visual Comput 23, 381–395 (2007). https://doi.org/10.1007/s00371-006-0094-3

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