Mesh Segmentation With Geodesic Means Clustering of Sharp Vertices

첨예정점의 측지거리 평균군집화를 이용한 메쉬 분할

  • 박영진 (충북대학교 정보산업공학과 및 충북대학교 컴퓨터교육과) ;
  • 박찬 (충북대학교 정보산업공학과 및 충북대학교 컴퓨터교육과) ;
  • 이위 (우석대학교 게임콘텐츠학과) ;
  • 하종성 (우석대학교 게임콘텐츠학과) ;
  • 유관희 (충북대학교 정보산업공학과 및 충북대학교 컴퓨터교육과)
  • Published : 2008.05.31


In this paper, we adapt the $\kappa$-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 $\kappa$ clusters by iterated converging with the $\kappa$-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 $\kappa$-means clustering method, it is crucial to assign an initial value to $\kappa$ appropriately. Hence, we automatically compute a reasonable number of clusters as an initial value of $\kappa$. Finally the mesh segmentation is completed by merging other vertices except the sharp vertices into the nearest cluster by geodesic distance.


Mesh Segmentation;Sharp Vertex;Geodesic Distance$\kappa$-means Clustering


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