Crease detection on noisy meshes via probabilistic scale selection

Abstract

Motivated by multi-scale edge detection in images, a novel multi-scale approach is presented to detect creases on 3D meshes. In this paper, we propose a probabilistic method to select local scales in the discrete 3D scale space. The likelihood function of local scale at each vertex is defined based on the minimum description length (MDL) principle. By introducing some prior knowledge, the optimal local scales are selected using Bayes rule. Therefore, the distribution of selected local scales is piecewise constant and discontinuity adaptive. The discrete 3D multi-scale representation of a given mesh can be constructed using an anisotropic diffusion method. With the selected scales, creases are traced by connecting the curvature extrema points detected on the mesh edges. Experimental results show that geometrically salient creases are well detected on noisy meshes using our method. © Springer-Verlag 2010.