Mesh denoising via total variation and weighted Laplacian regularizations

Abstract

Abstract Mesh denoising is a fundamental problem in geometry processing. The main challenge is to preserve sharp features (such as edges and corners) and smooth regions (such as smoothly curved regions and fine details) while removing the noise. State‐of‐the‐art denoising methods still struggle with this issue. In this paper, we first propose a new variational model combining total variation and anisotropic Laplacian regularization to filter the normal vector field of the mesh. This model can preserve sharp features and simultaneously handle smooth regions well. Then, a new vertex updating scheme is presented to reconstruct the mesh according to the filtered face normals. It prevents the orientation ambiguity problem introduced by existing schemes. Experiments show that our denoising method outperforms all compared methods visually and quantitatively, especially for meshes consisting of both sharp features and smooth regions.