Paper Info
Title
Non-rigid and Partial 3D Model Retrieval Using Hybrid Shape Descriptor and Meta Similarity.
Abstract
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.
Year
DOI
Venue
2012
10.1007/978-3-642-33179-4_20
ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT I
Field
DocType
Volume
Particle swarm optimization,Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Artificial intelligence,Adaptive algorithm,Geodesic
Conference
7431
ISSN
Citations 
PageRank 
0302-9743
2
0.38
References 
Authors
17
3
Name
Order
Citations
PageRank
Bo Li125715.09
Afzal Godil261930.70
Henry Johan335529.36