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.

2D image-based 3D objects retrieval is a new topic for 3D objects retrieval which can be used to manage 3D data with 2D images. The goal is to search some related 3D objects when given a 2D image. The task is challenging due to the large domain gap between 2D images and 3D objects. Therefore, it is essential to consider domain adaptation problems to reduce domain discrepancy. However, most of the existing domain adaptation methods only utilize the semantic information from the source domain to predict labels in the target domain and neglect the intrinsic structure of the target domain. In this paper, we propose a domain alignment framework with consistent domain structure learning to reduce the large gap between 2D images and 3D objects. The domain structure learning module makes use of both the semantic information from the source domain and the intrinsic structure of the target domain, which provides more reliable predicted labels to the domain alignment module to better align the conditional distribution. We conducted experiments on two public datasets, MI3DOR and MI3DOR-2, and the experimental results demonstrate the proposed method outperforms the state-of-the-art methods.

2D image-based 3D object retrieval is a very important task in computer vision and big data management. Conventional image-based 3D object retrieval usually assumes that the images are from one single domain. However, for real applications, 2D images may be from multiple domains (e.g., real image, sketch, and quick draw). It raises significant challenges for this task since these 2D images have a great domain gap with each other as well as a great modality gap with 3D objects. To address these issues, we propose an unsupervised Domain-Specific Alignment Network (DSAN) for multi-domain image-based 3D object retrieval. The proposed method aims to reduce domain discrepancy by domain-specific alignment network with multi-level moment matching, including first-order moment and second-order moment. Based on the observation that for any given sample, different domain classifiers should output the same label, we design a domain-specific classifier alignment module. To our knowledge, the proposed method is the first unsupervised work to align multiple-domain 2D images with 3D objects in an end-to-end manner. The multi-domain dataset MDI3D is utilized to advocate the research on this task, and the extensive experimental results demonstrate the superiority of the proposed method.

2D image-based 3D object retrieval is a novel but challenging task for 3D object retrieval. In this paper, we propose a 2D image-based 3D object retrieval method via joint heterogeneous feature learning and distribution alignment. Specifically, we propose to learn a mapping function in the Grassmann manifold to reduce the divergence of heterogeneous features of 2D images and 3D objects. We further employ the data distribution alignment method to adaptively integrate both marginal and conditional distributions. We embed both terms into the objective function to learn a domain-invariant classifier based on structural risk minimization. The output domain-invariant features of 2D images and 3D objects can be utilized for 2D image-based 3D object retrieval. Since there is lack of large-scale dataset for the evaluation of this task, we build two new datasets, MI3DOR and MI3DOR-2. We compare the proposed method against the representative methods for domain adaption and explore the influence of different components of the objective functions and key parameter. Comparison experiments show the superiority of this method.