HGAN: Holistic Generative Adversarial Networks for Two-dimensional Image-based Three-dimensional Object Retrieval

In this article, we propose a novel method to address the two-dimensional (2D) image-based 3D object retrieval problem. First, we extract a set of virtual views to represent each 3D object. Then, a soft-attention model is utilized to find the weight of each view to select one characteristic view for each 3D object. Second, we propose a novel Holistic Generative Adversarial Network (HGAN) to solve the cross-domain feature representation problem and make the feature space of virtual characteristic view more inclined to the feature space of the real picture. This will effectively mitigate the distribution discrepancies across the 2D image domains and 3D object domains. Finally, we utilize the generative model of the HGAN to obtain the virtual real image of each 3D object and make the characteristic view of the 3D object and real picture possess the same feature space for retrieval. To demonstrate the performance of our approach, We established a new dataset that includes pairs of 2D images and 3D objects, where the 3D objects are based on the ModelNet40 dataset. The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.