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

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