3D Model Comparison through Kernel Density Matching

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.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Yiming Wang, Tong Lu, Rongjun Gao, Wenyin Liu
• Keywords: Computer Vision, Distance Feature Pairs, Feature Pairs, ICPR 2010, Pre-processed 3D Models