3D similarity search using a weighted structural histogram representation
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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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.