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

Authors:
Shin Yoshizawa;Alexander Belyaev;Hans-Peter Seidel
Affiliations:
MPI Informatik, Germany;MPI Informatik, Germany;MPI Informatik, Germany
Venue:
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Year:
2006

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Abstract

In this paper, we propose a new and powerful shape denoising technique for processing surfaces approximated by triangle meshes and soups. 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 demonstrate that our technique outperforms recent state-of-the-art smoothing methods. We also suggest a new scheme for comparing different mesh/soup denoising methods.