Segmenting deformable soft-body meshes based on statistical variation information for piecewise Active Shape Model

This paper proposes an algorithm for segmenting deforming soft-body meshes based on statistical variation information extracted from the deforming meshes. The variation information is extracted by performing a global principal component analysis (PCA) on the set of meshes. eigen-variation similarity (EVS) and eigen-variation magnitude (EVM) are then defined for the vertices and triangle faces of the meshes based on the extracted variation information. A multiple-source region growing algorithm is presented for segmenting a mesh that favors grouping faces with similar variations into a same component. We apply the proposed mesh segmentation algorithm to the construction of piecewise active shape model (ASM) and use such piecewise ASM to reconstruct unseen meshes. Experimental results show that our algorithm outperforms several state-of-the-art methods in terms of reconstruction accuracy.