On Stochastic Methods for Surface Reconstruction (Articolo in rivista)

Type
Label
  • On Stochastic Methods for Surface Reconstruction (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s00371-006-0094-3 (literal)
Alternative label
  • Waqar Saleem; Oliver Schall; Giuseppe Patane'; Alexander Belyaev; Hans-Peter Seidel (2007)
    On Stochastic Methods for Surface Reconstruction
    in The visual computer; Springer Berlin / Heildelberg, Berlin (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Waqar Saleem; Oliver Schall; Giuseppe Patane'; Alexander Belyaev; Hans-Peter Seidel (literal)
Pagina inizio
  • 381 (literal)
Pagina fine
  • 395 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • FP6 Network of Excellence AIM@SHAPE Best Paper Award 2006. Saleem W., Schall O., Patane' G., Belyaev A., Seidel H. On stochastic methods for surface reconstruction. In: The Visual Computer, vol. 23 (6) pp. 381-395. Springer Berlin/Heidelberg, 2007. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/q7318h4331vmmr71/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 23 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 6 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Waqar Saleem Max-Planck-Institut Informatik (MPII), Saarbruecken, Germany Max-Planck-Institut Informatik (MPII), Saarbruecken, Germany Giuseppe Patane' IMATI-GE CNR, Genova, Italy Alexander Belyaev Max-Planck-Institut Informatik (MPII), Saarbruecken, Germany Hans-Peter Seidel Max-Planck-Institut Informatik (MPII), Saarbruecken, Germany (literal)
Titolo
  • On Stochastic Methods for Surface Reconstruction (literal)
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 un- certain data in the form of noise and outliers. We first present a method that filters out uncertain and redun- dant 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. (literal)
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