@techreport{oai:ipsj.ixsq.nii.ac.jp:00038149,
 author = {Hamid, Laga and Hiroki, Takahashi and Suguru, Saito and Masayuki, Nakajima and Hamid, Laga and Hiroki, Takahashi and Suguru, Saito and Masayuki, Nakajima},
 issue = {44(2005-CG-119)},
 month = {May},
 note = {In this paper we propose a simple method for the decomposition of point-sampled 3D objects into its basic components. Our approach is based on recent methods for fuzzy clustering and hierarchical decomposition of 3D meshes  where we use instead a k-nearest neighbor graph of the point cloud. Our approach proceeds in two steps: We first encode the geometric properties of the 3D shape into an inter-surfel distance matrix. The distance between two surfels takes into account the geodesic distance  the angular distance to measure the shape convexity and the surface variation. Then  we apply a clustering algorithm on the distance matrix to extract the different components of the input surfels. We demonstrate the efficiency of the proposed approach on a collection of point sampled 3D objects., In this paper we propose a simple method for the decomposition of point-sampled 3D objects into its basic components. Our approach is based on recent methods for fuzzy clustering and hierarchical decomposition of 3D meshes, where we use instead a k-nearest neighbor graph of the point cloud. Our approach proceeds in two steps: We first encode the geometric properties of the 3D shape into an inter-surfel distance matrix. The distance between two surfels takes into account the geodesic distance, the angular distance to measure the shape convexity and the surface variation. Then, we apply a clustering algorithm on the distance matrix to extract the different components of the input surfels. We demonstrate the efficiency of the proposed approach on a collection of point sampled 3D objects.},
 title = {A Multiscale Decomposition of Point-sampled 3D Objects},
 year = {2005}
}