Spatio-temporal segmentation for the similarity measurement of deforming meshes

Although there have been a large body of works on computing the similarity of static shapes, similarity judgments on deforming meshes are not studied well. In this study, we investigate a similarity measurement method for comparing two deforming meshes. Based on the degree of deformation, we first binarily label each triangle within each frame as either `deformed' or `rigid', then merge the `deformed' triangles in both spatial and temporal domains for the segmentation. The segmentation results are encoded in a form of evolving graph, with an aim of obtaining a compact representation of the motion of the mesh. Finally, we formulate the similarity measurement as a sequence matching problem: after clustering similar graphs and assigning each of the graphs with the cluster labels, each deforming mesh is represented with a sequence of labels. Then, we apply a sequence alignment algorithm to compute the locally optimal alignment between the two label sequences, and to compute the similarity by normalizing the alignment score. The experimental results over several datasets show that the similarities of animation data can be captured correctly using our approach. This may be significant, as it solves a problem that cannot be handled by current approaches.