Computer-Aided Geometric Design, 25(7):437-456, 2008.
ISSN 0167-8396.
Solid and Physical Modeling - Selected papers from the Solid and
Physical Modeling and Applications Symposium 2007 (SPM 2007).
[DOI: 10.1016/j.cagd.2007.12.008] [Preprint]
An approach to mesh denoising based on the concept of random walks is examined. The proposed method consists of two stages: face normal filtering, followed by vertex position updating to integrate the denoised face normals in a least-squares manner. Face normal filtering is performed by weighted averaging of normals in a neighbourhood. A novel approach to determining weights is to compute the probability of arriving at each neighbour following a fixed-length random walk of a virtual particle starting at a given face of the mesh. The probability of the particle stepping from its current face to some neighbouring face is a function of the angle between the two face normals, based on a Gaussian distribution whose variance is adaptively adjusted to enhance the feature-preserving property of the algorithm. The vertex position updating procedure uses the conjugate gradient algorithm for speed of convergence. Analysis and experiments show that random walks of different step lengths yield similar denoising results. Our experiments show that, in fact, iterative application of a one-step random walk in a progressive manner effectively preserves detailed features while denoising the mesh very well. This approach is faster than many other feature-preserving mesh denoising algorithms.
@ARTICLE{Sun2008,
author = {Xianfang Sun and Paul L. Rosin and Ralph R. Martin
and Frank C. Langbein},
title = {Random walks for feature-preserving mesh denoising},
journal = {Computer-Aided Geometric Design},
year = {2008},
volume = {25},
pages = {437-456},
number = {7},
issn = {0167-8396}
doi = {10.1016/j.cagd.2007.12.008},
url = {http://www.langbein.org/research/surfaces/filtering/sun2008/}
abstract = {An approach to mesh denoising based on the concept
of random walks is examined. The proposed method
consists of two stages: face normal filtering,
followed by vertex position updating to integrate
the denoised face normals in a least-squares manner.
Face normal filtering is performed by weighted
averaging of normals in a neighbourhood. A novel
approach to determining weights is to compute the
probability of arriving at each neighbour following
a fixed-length random walk of a virtual particle
starting at a given face of the mesh. The
probability of the particle stepping from its
current face to some neighbouring face is a function
of the angle between the two face normals, based on
a Gaussian distribution whose variance is adaptively
adjusted to enhance the feature-preserving property
of the algorithm. The vertex position updating
procedure uses the conjugate gradient algorithm for
speed of convergence. Analysis and experiments show
that random walks of different step lengths yield
similar denoising results. Our experiments show
that, in fact, iterative application of a one-step
random walk in a progressive manner effectively
preserves detailed features while denoising the mesh
very well. This approach is faster than many other
feature-preserving mesh denoising algorithms.},
}
Random Walks for Feature-Preserving Mesh Denoising,http://www.langbein.org/research/manifolds/filtering/sun2008/ by Frank C Langbein [26/October/2008, 17:01].
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