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On Stochastic Methods for Surface Reconstruction

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45337

Saleem,  Waqar
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45372

Schall,  Oliver
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45168

Patanè,  Giuseppe
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44112

Belyaev,  Alexander
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Saleem, W., Schall, O., Patanè, G., Belyaev, A., & Seidel, H.-P. (2007). On Stochastic Methods for Surface Reconstruction. The Visual Computer, 23(6), 381-395. doi:10.1007/s00371-006-0094-3.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-201B-5
Zusammenfassung
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.