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 features. We also develop an adaptive algorithm to generate meta similarity resulting from different component features of the hybrid shape descriptor based on Particle Swarm Optimization. Experimental results demonstrate the effectiveness and advantages of our framework. It 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.
Proceedings Title: Lecture Notes in Computer Science, Advances in Visual Computing, The 8th International Symposium on Visual Computing (ISVC 2012), Springer
Conference Dates: July 16-18, 2012
Conference Location: Crete, -1
Conference Title: 8th International Symposium on Visual Computing
Pub Type: Conferences
Non-rigid 3D Model Retrieval, Partial 3D model retrieval, Hybrid shape descriptor, Meta similarity, Particle Swarm Optimization