Triangulated surface denoising using high order regularization with dynamic weights

作     者：Liu, Zheng Lai, Rongjie Zhang, Huayan Wu, Chunlin 

作者机构：National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan China Department of Mathematics Rensselaer Polytechnic Institute United States School of Computer Science and Software Tianjin Polytechnic University Tianjin China School of Mathematical Sciences Nankai University Tianjin China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2017年

核心收录：

主　　题：Numerical methods 

摘      要：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 0regularization methods. However, these two methods have su ered 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. Copyright © 2017, The Authors. All rights reserved.