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Smoothing by Example: Mesh Denoising by Averaging with Similarity-based Weights

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45777

Yoshizawa,  Shin
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44112

Belyaev,  Alexander
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44112

Belyaev,  A.
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Yoshizawa, S., Belyaev, A., & Seidel, H.-P. (2006). Smoothing by Example: Mesh Denoising by Averaging with Similarity-based Weights. In IEEE International Conference on Shape Modeling and Applications 2006 (SMI 2006) (pp. 38-44). California, USA: IEEE.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-23F4-E
Zusammenfassung
In this paper, we propose a new and powerful mesh/soup denoising technique. Our approach is inspired by recent non-local image denoising schemes and naturally extends bilateral mesh smoothing methods. The main idea behind the approach is very simple. A new position of vertex $P$ of a noisy mesh is obtained as a weighted mean of mesh vertices $Q$ with nonlinear weights reflecting a similarity between local neighborhoods of $P$ and $Q$. We demonstrated that our technique outperforms recent state-of-the-art smoothing methods. We also suggest a new approach for comparing different mesh/soup denoising methods.