View-based 3D model retrieval via supervised multi-view feature learning

With the development of the processing technologies of 3D model and the increasing of 3D model in different application flieds, 3D model retrieval is attracting more and more people’s attention. In order to handle this problem, most of approaches focus on the feature extraction form different virtual view. It is hard to guarantee the robustness and also ignore the correlation between both views. Thus, we propose an effective view-based 3D model retrieval method via supervised multi-view feature learning (SMFL). First, the subspace dimension of viusal feature is generated through Singular Value Decomposition (SVD) algorithm. This step is used to select main information from multi-view in order to reduce the final amount of calculation; Secondly, we consider the relationship of multi-view from same class and the correlation between two different classes to make the feature mapping in order to reduce the different of views from the same class and increase the different of views from the difference class; Finally, the projection mapping corresponding to the inner product of each 3D model helps to calculate the similarities between two different 3D models. The extensive experiments are conducted on popular ETH, NTU, MV-RED and PSB 3D model datasets with Zernike moments. The comparative results or The experimental results with existing 3D model retrieval methods show the superiority of the proposed method.