Shape-aware Mesh Normal Filtering. (November 2021) - Zhong, Saishang
Abstract: Mesh denoising is a fundamental yet open problem in geometry processing. The main challenge is to remove noise while recovering the shape of the underlying surface as accurately as possible. In this paper, we propose a novel joint bilateral filter on the face normal field. The key is to estimate a reliable guidance normal field by constructing a shape-aware consistent patch for accurately describing the local shape of each face. To this end, we first select a candidate patch for each face by using a newly defined consistent metric considering both patch flatness and face-to-patch orientation similarity. Then, spectral analysis is used in combination with ℓ 0 minimization to refine the candidate patches in a shape-aware manner. The refined patches do not contain any features, and therefore they can accurately describe the local shape of the underlying surface. After smoothing the face normal field, vertex positions are reconstructed to match the filtered face normals. Our mesh denoising method is theoretically rooted and practical for dealing with the meshes containing corners with low sampling rates, multi-scale features, or narrow structure regions. Extensive experimental results demonstrate that our method can significantly improve the feature preserving capability of joint normal filter and outperforms state-of-the-art methods visually and quantitatively. Graphical abstract: Highlights: A simple yet effective consistency metric is designed to select candidate patches. A spectral partitioning method is introduced to produce shape-aware patches. A variety of experiments to show that our method outperforms the state-of-the-arts.