Non-rigid and partial 3D model retrieval are two significant and challenging research directions in the field of 3D model retrieval. Little work has been done in proposing a hybrid shape descriptor that works for both retrieval scenarios, let alone the integration of the component features of the hybrid shape descriptor in an adaptive way. In this paper, we propose a hybrid shape descriptor that integrates both geodesic distance- based global features and curvature-based local fea- tures. We also develop an adaptive algorithm to gener- ate meta similarity resulting from different component features of the hybrid shape descriptor based on Parti- cle Swarm Optimization. Experimental results demon- strate the effectiveness and advantages of our frame- work, as well as the significant improvements in re- trieval performances. The framework is general and can be applied to similar approaches that integrate more features for the development of a single algorithm for both non-rigid and partial 3D model retrieval.
Citation: Multimedia Tools and Applications
Pub Type: Journals
3D model retrieval , Non-rigid models , Partial similarity retrieval , Hybrid shape descriptor , Meta similarity