Mesh Denoising via a Novel Mumford–Shah Framework

• DOI: 10.1016/j.cad.2020.102858
• Publication Date: 2020-05-04T19:58:13Z
• Authors: Zheng Liu; Weina Wang; Saishang Zhong; Bohong Zeng; Jinqin Liu; Weiming Wang
• Keywords: Discontinuity (linguistics); Classification of discontinuities; Pointwise; Function space

Abstract In this paper, we introduce a Mumford–Shah framework to restore the face normal field on the triangulated surface. To effectively discretize Γ -convergence approximation of the Mumford–Shah model, we first define an edge function space and its associated differential operators. They are helpful for directly diffusing the discontinuity function over mesh edges instead of computing the approximated discontinuity function via pointwise diffusion in existing discretizations. Then, by using the operators in the proposed function space, two Mumford–Shah-based denoising methods are presented, which can produce denoised results with neat geometric features and locate geometric discontinuities exactly. Our Mumford–Shah framework overcomes the limitations of existing techniques that blur the discontinuity function, be less able to preserve geometric features, be sensitive to surface sampling, and require a postprocessing to form feature curves from located discontinuity vertices. Intensive experimental results on a variety of surfaces show the superiority of our denoising methods qualitatively and quantitatively.