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Abstract

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.