3D Scattered Data Approximation with Adaptive Compactly Supported Radial Basis 
Functions

Ohtake, Yutaka; Belyaev, Alexander; Seidel, Hans-Peter

doi:10.1109/SMI.2004.1314491

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3D Scattered Data Approximation with Adaptive Compactly Supported Radial Basis Functions

MPS-Authors

/persons/resource/persons45141

Ohtake, Yutaka
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons44112

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

/persons/resource/persons45449

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

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引用

Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2004). 3D Scattered Data Approximation with Adaptive Compactly Supported Radial Basis Functions. In F., Giannini, & A., Pasko (Eds.), Shape Modeling International 2004 (pp. 31-39). Los Alamitos, USA: IEEE.

引用: https://hdl.handle.net/11858/00-001M-0000-000F-29F1-6

要旨

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.