Multi Modal Clique Graph Matching for View Based 3D Model Retrieval
Multi view matching is an important but a challenging task in view based 3D model retrieval. To address this challenge, we propose an original multi modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi modal feature space and hyper edges that link pairwise cliques. Moreover, we propose an image set based clique/edgewise similarity measure to address the issue of the set to set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high order attributes and solving hyper graph matching. We validate the MCG based 3D model retrieval using three popular single modal data sets and one novel multi modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real world 3D object data set, the multi view RGB D object data set. To the best of our knowledge, it is the largest real world 3D object data set containing multi modal and multi view information.
IEEE Transactions on Image Processing