Multi-Feature Fusion Based on Multi-View Feature and 3D Shape Feature for Non-Rigid 3D Model Retrieval

Abstract

To make full use of the effective discriminative information of the non-rigid 3D model, we propose a novel multi-feature fusion method to fuse the multi-view feature and the 3D shape feature and apply it in a non-rigid 3D model retrieval. First, we compute the canonical form of the non-rigid 3D model using the biharmonic distance-based least-squares multidimensional scaling (LS-MDS) algorithm and generate multiple projective depth images. The learning-based multiple pooling fusion methods is used in the multi-view convolutional neural network to reduce the information loss and extract more effective multi-view feature. Then, we compute the wave kernel signature of each vertex and construct the multi-energy shape distribution of the non-rigid 3D model. The convolutional neural network is used for learning the 3D shape feature. Finally, we use the kernel canonical correlation analysis (KCCA) algorithm to fuse the multi-view feature and the 3D shape feature for retrieval. Our experimental results have shown that compared with the geodesic distance-based LS-MDS algorithm, the biharmonic distance-based LS-MDS algorithm has higher computation efficiency and better performance. Compared with other state-of-the-art methods, our proposed method can make better use of the two kinds of features and has achieved better retrieval results.