X.-F. Sun, P. L. Rosin, R. R. Martin, F. C. Langbein
In: Proc. ACM Symp. Solid and Physical Modeling,
pp. 11-22, ACM Siggraph 2007.
ISBN 1595936660.
[DOI: 10.1145/1236246.1236252] [Preprint] [CiteSeer]
This paper considers an approach to mesh denoising based on the concept of random walks. The proposed method consists of two stages: a face normal filtering procedure, followed by a vertex position updating procedure which integrates the denoised face normals in a least-squares sense. Face normal filtering is performed by weighted averaging of normals in a neighbourhood. The weights are based on the probability of arriving at a given neighbour after a random walk of a virtual particle starting at a given face of the mesh and moving a fixed number of steps. The probability of a particle stepping from its current face to a given neighboring face is determined by the angle between the two face normals, using a Gaussian distribution whose width 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. In particular, iterative application of a one-step random walk in a progressive manner effectively preserves detailed features while denoising the mesh very well. We observe that this approach is faster than many other feature-preserving mesh denoising algorithms.
@INPROCEEDINGS{Sun2007a,
author = {Xianfang Sun and Paul L. Rosin and Ralph R. Martin
and Frank C. Langbein},
title = {Random Walks for Mesh Denoising},
booktitle = {Proc. ACM Symp. Solid and Physical Modeling},
year = 2007,
pages = {11-22},
address = {New York, NY, USA},
publisher = {ACM Siggraph},
doi = {10.1145/1236246.1236252},
isbn = 1595936660,
url = {http://www.langbein.org/research/surfaces/filtering/sun2007a/},
abstract = {This paper considers an approach to mesh denoising
based on the concept of random walks. The proposed
method consists of two stages: a face normal
filtering procedure, followed by a vertex position
updating procedure which integrates the denoised
face normals in a least-squares sense. Face normal
filtering is performed by weighted averaging of
normals in a neighbourhood. The weights are based on
the probability of arriving at a given neighbour
after a random walk of a virtual particle starting
at a given face of the mesh and moving a fixed
number of steps. The probability of a particle
stepping from its current face to a given
neighboring face is determined by the angle between
the two face normals, using a Gaussian distribution
whose width 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. In particular, iterative
application of a one-step random walk in a
progressive manner effectively preserves detailed
features while denoising the mesh very well. We
observe that this approach is faster than many other
feature-preserving mesh denoising algorithms.},
}
Random Walks for Mesh Denoising,http://www.langbein.org/research/manifolds/filtering/sun2007a by Frank C Langbein [ 6/December/2008, 19:28].
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