Recently, the view-based 3D model retrieval methods have received great research attentions. However, these methods are difficult to preserve the spatial structure of 3D models. In this paper, we propose a novel view-based 3D model retrieval method to solve this problem. Our method is based on the two-level (3D model-level and 2D image-level) spatial structure. Firstly, we extract the spatial structure circular descriptor (SSCD) images from 3D models. The SSCD images can preserve the spatial structure on the 3D model-level. Then, we modify the bag-of-features (BOF) method to extract view-based features from these SSCD images. The modified BOF method can preserve the spatial structure on the 2D image-level. Finally, we calculate the similarity between the query model and the models in the databases by adapting the earth mover distance method. Experimental results show that our method can achieve satisfactory retrieval performance for both the articulated models and the rigid models.

With the rapidly increasing of 3D models, the view-based 3D model retrieval methods have received significant research attention. The previous view-based methods can achieve ideal retrieval result for the rigid models, but they only obtain poor retrieval result for the deformable models because they can not preserve the topological structure well. In this paper, we propose a view-based 3D model retrieval algorithm using topological structure. We extract the view-based features from the images rendered at the salient topological points. To preserve the topological structure of the 3D model, a multiresolutional reeb graph (MRG) is constructed according to the salient topological points. We take the view-based features as the attribute information of the corresponding MRG nodes. The comparison between two 3D models is transformed to compute the similarity of the corresponding MRGs. Experimental results on two standard benchmarks show that our algorithm can achieve satisfactory retrieval performance for both the deformable models and the rigid models.

The number of available non-rigid 3D models in various areas increases steadily. The local features are more effective than global features for the search of these non-rigid 3D models. Global descriptors fail to consistently compensate for the intra-class variability of non-rigid 3D models. To solve this problem, we propose a non-rigid 3D model retrieval method based on multi-scale local features. Firstly, we extract keypoints at multiple scales automaticlly. Then, the Heat Kernel Signature (HKS) local descriptors are computed for each keypoint. However, the HKS descriptors are sensitive to scale. In order to solve this problem, the HKS descriptors are put into the Bag-of-Features (BOF) framework. In the BOF framework, we use a kind of histogram equalization technique to make our feature descriptor robust to model scaling. Experimental results on two public benchmarks show that our algorithm can achieve satisfactory retrieval performance for the non-rigid 3D models.