A 3D shape matching and retrieval approach based on fusion of curvature and geometric diffusion features

The majority of shape matching and retrieval methods use only one single shape descriptor. Unfortunately, no shape descriptor is sufficient to provide suitable results for all kinds of shapes. The most common way to improve the performance of shape descriptors is to fuse them. In this paper, we propose a new 3D matching and retrieval approach based on a fully unsupervised fusion of curvature and geometric diffusion descriptors. In fact, to improve retrieval precision, we use two descriptors based on local and global features extracted from a shape, and automatically combine these features using a fusion method called Product rule. The Product rule combines values assigned to vertices by the two descriptors. This fusion rule gives better results compared to other well-known fusion schemes such as Max, Min and Linear rules. The proposed approach improves considerably the retrieval precision even with pose changes. This is shown through the retrieval results obtained on several popular 3D shape benchmarks.