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3D Model Comparison through Kernel Density Matching

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4 Author(s)
Yiming Wang ; State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China ; Tong Lu ; Rongjun Gao ; Wenyin Liu

A novel 3D shape matching method is proposed in this paper. We first extract angular and distance feature pairs from pre-processed 3D models, then estimate their kernel densities after quantifying the feature pairs into a fixed number of bins. During 3D matching, we adopt the KL-divergence as a distance of 3D comparison. Experimental results show that our method is effective to match similar 3D shapes, and robust to model deformations or rotation transformations.

Published in:

Pattern Recognition (ICPR), 2010 20th International Conference on

Date of Conference:

23-26 Aug. 2010