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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.
