Three-Dimensional Model Similarity Analysis Based on Salient Features Spectral Embedding
HAN Li1,2, YAN Zhen1, XU Jian-Guo1, TANG Di1
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116081 2.Faculty of Computer Science and Technology, Dalian University of Technology, Dalian 116024
Abstract Aiming at the requirement of efficient 3D model retrieval technology, a three-dimensional model similarity analysis based on salient features spectral embedding method is proposed. Firstly, the salient features are extracted by curvature-based method and a convex-concave measurement to build the salient features representation for the shape. Then these features are embedded in a spectral domain to reveal the intrinsic shape characteristics based on Laplacian Eigenmap. Finally, combined with the thin plate splines method, the model similarity analysis and registration are implemented. The experimental results show that by using the proposed method shape matching is implemented efficiently and the consistent structural features in same category models are identified. Moreover, it is robust to the imperfect shape matching.
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2015, Vol. 28 
