Combining topological and view-based features for 3D model retrieval

Pengjie Li, Huadong Ma, Anlong Ming

Abstract: With the rapidly increasing of 3D models, the 3D model retrieval methods have been paid significant research attention. Most of the existing methods focus on taking advantage of one kind of feature. These methods can not achieve ideal retrieval results for different classes of 3D models. In this paper, we propose a novel 3D model retrieval algorithm by combining topological and view-based features. To preserve the topological structure of the 3D model, a multiresolutional reeb graph (MRG) is constructed according to the salient topological points. The view-based features are extracted from the images, which are rendered at each of the topological points. To preserve the spatial structure information of the images, we modify the bag-of-features (BOF) method by using the combined shell-sector model. We take the view-based features as the attribute information of the corresponding MRG nodes. The comparison between two 3D models is transformed to the problem of computing the similarity of the corresponding MRGs. Finally, we calculate the similarity between the query model and the models in the databases by adapting the earth mover distance method. Experimental results on two standard benchmarks show that our algorithm can achieve satisfactory retrieval performance.