Random Walks for Mesh Denoising Export

@inproceedings{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.}, address = {New York, NY, USA}, author = {Sun, Xianfang and Rosin, Paul L. and Martin, Ralph R. and Langbein, Frank C.}, booktitle = {Proc. ACM Symp. Solid and Physical Modeling}, citeulike-article-id = {3776259}, citeulike-linkout-0 = {http://dx.doi.org/10.1145/1236246.1236252}, citeulike-linkout-1 = {http://www.langbein.org/research/surfaces/filtering/sun2007a/}, doi = {10.1145/1236246.1236252}, keywords = {denoising, frankclangbein, random-walks}, pages = {11--22}, posted-at = {2008-12-11 23:48:09}, priority = {0}, publisher = {ACM Siggraph}, title = {Random Walks for Mesh Denoising}, url = {http://dx.doi.org/10.1145/1236246.1236252}, year = {2007} }

TY - CONF ID - Sun2007a L3 - citeulike-article-id:3776259 N2 - 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. AD - New York, NY, USA EP - 22 TI - Random Walks for Mesh Denoising PB - ACM Siggraph T2 - Proc. ACM Symp. Solid and Physical Modeling SP - 11 KW - denoising KW - frankclangbein KW - random-walks AU - Sun, Xianfang AU - Rosin, Paul AU - Martin, Ralph AU - Langbein, Frank PY - 2007/// UR - http://dx.doi.org/10.1145/1236246.1236252 ER -