3D mesh saliency from local spiral hop descriptors.

Mesh saliency, the process of detecting visually important regions in 3D meshes, is a significant component in computer graphics, that can be used in various applications such as denoising and simplification. In this paper, we propose a new 3D mesh saliency measure that can identify sharp geometric features in meshes. A local normal-based descriptor is built for each vertex thanks to a spiral path within a 2-hop neighborhood. First, a geometric-based saliency is computed as the mean local alignment between the spiral descriptors within a 1-hop, and weighted by a vertex roughness measure. Second, a spectral-based saliency is computed from the spectral energy of each vertex structure tensor with the gradient defined from the spiral descriptor alignments. The final saliency is then defined as a weighted sum of both. This single-scale saliency can be extended to a multi-scale saliency by decimating the mesh at several scales and averaging back the obtained saliencies after mapping them between decimated meshes. The approach presents competitive results with state-of-the-art.