Joint Intermediate Domain Generation and Distribution Alignment for 2D Image-Based 3D Objects Retrieval

2D image-based 3D object retrieval provides a convenient way to manage 3D big data with easily accessed 2D images. It is also a challenging task due to the significant differences between 2D images and 3D objects. In this paper, we propose a 2D image-based 3D object retrieval method, which can reduce the distribution discrepancy between 2D images and 3D objects and learn invariant features between them. Specifically, we first construct an intermediate domain module based on maximum mean discrepancy (MMD) in an unsupervised way, which can reduce the 2D and 3D distribution discrepancy by marginal distribution constraint. Second, to further reduce conditional distribution discrepancy and learn invariant features, we use source domain labels as semantic information to dynamically guide distribution alignment. Moreover, in order to support the research in 3D object retrieval, we contribute a new dataset, MDI3D. We conducted extensive experiments on MDI3D and some popular datasets, such as MI3DOR and SHREC2013. The experimental results demonstrate the superiority of the proposed method by comparing with the state-of-the-art methods.