Multi-view-based siamese convolutional neural network for 3D object retrieval.

Representing three-dimensional (3D) objects by multiple views has become a common solution to the problem of 3D object retrieval. It has gained excellent achievements because of its remarkable adaptability and flexibility. In this paper, we develop a multi-view-based Siamese convolutional neural network for 3D object retrieval. It consists of two sub-networks which have the same architecture and also share the same set of weights. First, we generate a set of RGB images and binary images for each 3D object to capture local and global features. Second, the two sub-networks take corresponding images as input and avoid camera constraint by using average fusion layers. The final compact descriptors are learned by integrating features of sub-networks and then used for retrieval. Experimental results on two benchmarks, the PSB dataset and ETH dataset, testify that our proposed method receives superior retrieval performance compared to state-of-the-art methods.