Organic skeleton correspondence using part arrangements

Shape correspondence between semantically similar organic shapes with large shape variations is a difficult problem in shape analysis.Since part geometries are no longer similar,we claim that the challenge is to extract and compare prominent shape substructures,which are recurring part arrangements among semantically related shapes.Our main premise is that the challenge can be solved more efficiently on curve skeleton graphs of shapes,which provide a concise abstraction of shape geometry and structure.Instead of directly searching exponentially many skeleton subgraphs,our method extracts the intrinsic reflectional symmetry axis of the skeleton to guide the generation of subgraphs as part arrangements.For any two subgraphs from two skeletons,their orientations are aligned and their pose variations are normalized for matching.Finally,the matchings of all subgraph pairs are evaluated and accumulated to the skeletal feature node correspondences.The comparison results with the state-of-the-art work show that our method significantly improves the efficiency and accuracy of the semantic correspondence between a variety of shapes.

Shape correspondence between semantically similar organic shapes with large shape variations is a diffcult problem in shape analysis. Since part geometries are no longer similar,we claim that the challenge is to extract and compare prominent shape substructures, which are recurring part arrangements among semantically related shapes. Our main premise is that the challenge can be solved more effciently on curve skeleton graphs of shapes, which provide a concise abstraction of shape geometry and structure. Instead of directly searching exponentially many skeleton subgraphs, our method extracts the intrinsic reflectional symmetry axis of the skeleton to guide the generation of subgraphs as part arrangements. For any two subgraphs from two skeletons, their orientations are aligned and their pose variations are normalized for matching. Finally, the matchings of all subgraph pairs are evaluated and accumulated to the skeletal feature node correspondences. The comparison results with the state-of-the-art work show that our method significantly improves the effciency and accuracy of the semantic correspondence between a variety of shapes.