Mesh Denoising

TL;DR

This paper compares four mesh denoising techniques, analyzing their effectiveness and efficiency on different mesh sizes, highlighting the strengths and limitations of each approach.

Contribution

It provides an empirical comparison of four mesh denoising methods, including heat diffusion and Sobolev regularization, with insights into their performance on various mesh sizes.

Findings

Sobolev regularization is the fastest method.

Heat diffusion is slower and less effective on large meshes.

All methods perform better on larger meshes.

Abstract

In this paper, we study four mesh denoising methods: linear filtering, a heat diffusion method, Sobolev regularization, and, to a lesser extent, a barycentric approach based on the Sinkhorn algorithm. We illustrate that, for a simple image denoising task, a naive choice of a Gibbs kernel can lead to unsatisfactory results. We demonstrate that while Sobolev regularization is the fastest method in our implementation, it produces slightly less faithful denoised meshes than the best results obtained with iterative filtering or heat diffusion. We empirically show that, for the large mesh considered, the heat diffusion method is slower and not more effective than filtering, whereas on a small mesh an appropriate choice of diffusion parameters can improve the quality. Finally, we observe that all three mesh-based methods perform markedly better on the large mesh than on the small one.