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.

翻译：以特征保持为目标的网格去噪在视觉媒体领域受到广泛关注，旨在从受噪声污染的网格中恢复高保真、洁净的网格形状。现有去噪方法通常为各向异性表面分配较小权重，各向同性表面分配较大权重，以保留网格形状上的尖锐特征（如边缘或角点）。然而，这些方法往往忽略了各向异性表面上这种较小权重的设置仍会对去噪结果及形状细节保持效果产生负面影响。本文提出一种新颖的分段驱动网格去噪方法，该方法通过区域级去噪避免各向异性邻接面的干扰，从而实现更优的特征保持效果。此外，我们的主体框架可便捷嵌入常用网格去噪框架中。大量实验表明，该方法能在合成与真实网格模型上，从定量指标与视觉效果两方面有效提升去噪性能。