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Journal Article

Sparse Surface Reconstruction with Adaptive Partition of Unity and Radial Basis Functions

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

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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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Citation

Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2006). Sparse Surface Reconstruction with Adaptive Partition of Unity and Radial Basis Functions. Graphical Models, 68(1), 15-24. doi:10.1016/j.gmod.2005.08.001.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-23F6-A
Abstract
A new implicit surface fitting method for surface reconstruction
from scattered point data is proposed. The method combines an
adaptive partition of unity approximation with least-squares RBF
fitting and is capable of generating a high quality surface
reconstruction. Given a set of points scattered over a smooth surface,
first a sparse set of overlapped local approximations is constructed.
The partition of unity generated from these local
approximants already gives a faithful surface reconstruction.
The final reconstruction is obtained by adding compactly supported
RBFs. The main feature of the developed approach consists of
using various regularization schemes which lead to economical,
yet accurate surface reconstruction.