  Feature-preserving mesh denoising has received noticeable attention in visual
media, with the aim of recovering high-fidelity, clean mesh shapes from the
ones that are contaminated by noise. Existing denoising methods often design
smaller weights for anisotropic surfaces and larger weights for isotropic
surfaces in order to preserve sharp features, such as edges or corners, on the
mesh shapes. However, they often disregard the fact that such small weights on
anisotropic surfaces still pose negative impacts on the denoising outcomes and
detail preservation results on the shapes. In this paper, we propose a novel
segmentation-driven mesh denoising method which performs region-wise denoising,
and thus avoids the disturbance of anisotropic neighbour faces for better
feature preservation results. Also, our backbone can be easily embedded into
commonly-used mesh denoising frameworks. Extensive experiments have
demonstrated that our method can enhance the denoising results on a wide range
of synthetic and real mesh models, both quantitatively and visually.
