A Meaningful Mesh Segmentation Based on Local Self-similarity Analysis

On the basis of minima rule from the cognitive theory, this paper presents an algorithm decomposing a mesh into smaller parts by feature contours, gotten from the minima negative curvature vertices. The algorithm is carried out in two steps. Firstly, to avoid over-segmentation, our method excludes unimportant local adjacent similar contours. Secondly, the remnant salient contours are automatically completed to form short loops around mesh´s parts, constrained by two near parallel cutting planes that are determined by principal component analysis of all vertices. The algorithm has been demonstrated on many meshes, and the results show that it not only can perceptual group the adjacent self-similarity regions, but also can achieve reasonable segmentations.