Frank C Langbein
Ex Tenebris Scientia




X.-F. Sun, P. L. Rosin, R. R. Martin, F. C. Langbein.

IEEE Trans. Visualization and Computer Graphics, 13(5):925-938, 2007.
ISSN 10772626.

[DOI: 10.1109/TVCG.2007.1065] [Preprint] [CiteSeer]

We present a simple and fast mesh denoising method, which can remove noise effectively while preserving mesh features such as sharp edges and corners. The method consists of two stages. First, noisy face normals are filtered iteratively by weighted averaging of neighboring face normals. Second, vertex positions are iteratively updated to agree with the denoised face normals. The weight function used during normal filtering is much simpler than that used in previous similar approaches, being simply a trimmed quadratic. This makes the algorithm both fast and simple to implement. Vertex position updating is based on the integration of surface normals using a least-squares error criterion. Like previous algorithms, we solve the least-squares problem by gradient descent; whereas previous methods needed user input to determine the iteration step size, we determine it automatically. In addition, we prove the convergence of the vertex position updating approach. Analysis and experiments show the advantages of our proposed method over various earlier surface denoising methods. 

@ARTICLE{Sun2007,
  author =       {Xianfang Sun and Paul L. Rosin and Ralph R. Martin
                  and Frank C. Langbein},
  title =        {Fast and Effective Feature-Preserving Mesh
                  Denoising},
  journal =      {IEEE Trans. Visualization and Computer Graphics},
  year =         2007,
  volume =       13,
  pages =        {925-938},
  number =       5,
  issn =         10772626,
  doi =          {10.1109/TVCG.2007.1065},
  url =          {http://www.langbein.org/research/surfaces/filtering/sun2007/},
  abstract =     {We present a simple and fast mesh denoising method,
                  which can remove noise effectively while preserving
                  mesh features such as sharp edges and corners. The
                  method consists of two stages. First, noisy face
                  normals are filtered iteratively by weighted
                  averaging of neighboring face normals. Second,
                  vertex positions are iteratively updated to agree
                  with the denoised face normals. The weight function
                  used during normal filtering is much simpler than
                  that used in previous similar approaches, being
                  simply a trimmed quadratic. This makes the algorithm
                  both fast and simple to implement. Vertex position
                  updating is based on the integration of surface
                  normals using a least-squares error criterion. Like
                  previous algorithms, we solve the least-squares
                  problem by gradient descent; whereas previous
                  methods needed user input to determine the iteration
                  step size, we determine it automatically. In
                  addition, we prove the convergence of the vertex
                  position updating approach. Analysis and experiments
                  show the advantages of our proposed method over
                  various earlier surface denoising methods.},
}
Cite as Fast and Effective Feature-Preserving Mesh Denoising, http://www.langbein.org/research/manifolds/filtering/sun2007/print by Frank C Langbein [13/June/2009, 17:52].