첨예정점의 측지거리 평균군집화를 이용한 메쉬 분할

Abstract

In this paper, we adapt the -means clustering technique to segmenting a given 3D mesh. In order to avoid the locally minimal convergence and speed up the computing time, first we extract sharp vertices from the mesh by analysing its curvature and convexity that respectively reflect the local and global geometric characteristics from the viewpoint of cognitive science. Next the sharp vertices are partitioned into clusters by iterated converging with the -means clustering method based on the geodesic distance instead of the Euclidean distance between each pair of the sharp vertices. For obtaining the effective result of -means clustering method, it is crucial to assign an initial value to appropriately. Hence, we automatically compute a reasonable number of clusters as an initial value of . Finally the mesh segmentation is completed by merging other vertices except the sharp vertices into the nearest cluster by geodesic distance.