Triangulated Surface Denoising using High Order Regularization with Dynamic Weights

TL;DR

This paper introduces a high order regularization approach with dynamic weights for denoising triangulated surfaces, effectively preserving sharp features and reducing staircase artifacts in smooth regions.

Contribution

It proposes a novel second order regularization model combined with a new vertex updating scheme, improving feature preservation and artifact suppression in surface denoising.

Findings

Outperforms existing methods in preserving sharp features.

Reduces staircase artifacts in smooth regions.

Demonstrates superior results through extensive experiments.

Abstract

Recovering high quality surfaces from noisy triangulated surfaces is a fundamental important problem in geometry processing. Sharp features including edges and corners can not be well preserved in most existing denoising methods except the recent total variation (TV) and $\ell_0$ regularization methods. However, these two methods have suffered producing staircase artifacts in smooth regions. In this paper, we first introduce a second order regularization method for restoring a surface normal vector field, and then propose a new vertex updating scheme to recover the desired surface according to the restored surface normal field. The proposed model can preserve sharp features and simultaneously suppress the staircase effects in smooth regions which overcomes the drawback of the first order models. In addition, the new vertex updating scheme can prevent ambiguities introduced in existing vertex updating methods. Numerically, the proposed high order model is solved by the augmented Lagrangian method with a dynamic weighting strategy. Intensive numerical experiments on a variety of surfaces demonstrate the superiority of our method by visually and quantitatively.