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11

ICPR
2010
IEEE

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3D Model Comparison through Kernel Density Matching

13 years 10 months ago

3D Model Comparison through Kernel Density Matching

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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.

Yiming Wang, Tong Lu, Rongjun Gao, Wenyin Liu

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Computer Vision | Distance Feature Pairs | Feature Pairs | ICPR 2010 | Pre-processed 3D Models |

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Added	23 Jun 2010
Updated	23 Jun 2010
Type	Conference
Year	2010
Where	ICPR
Authors	Yiming Wang, Tong Lu, Rongjun Gao, Wenyin Liu

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