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3D scattered data approximation with adaptive compactly supported radial basis functions

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45141

Ohtake,  Yutaka
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

Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2004). 3D scattered data approximation with adaptive compactly supported radial basis functions. In Shape Modeling International 2004 (SMI 2004) (pp. 31-39). Los Alamitos, USA: IEEE.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-29F1-6
Zusammenfassung
In this paper, we develop an adaptive RBF fitting procedure for a high quality approximation of a set of points scattered over a piecewise smooth surface. We use compactly supported RBFs whose centers are randomly chosen from the points. The randomness is controlled by the point density and surface geometry. For each RBF, its support size is chosen adaptively according to surface geometry at a vicinity of the RBF center. All these lead to a noise-robust high quality approximation of the set. We also adapt our basic technique for shape reconstruction from registered range scans by taking into account measurement confidences. Finally, an interesting link between our RBF fitting procedure and partition of unity approximations is established and discussed.