With the widespread application of 3D capture devices, diverse 3D object datasets from different domains have emerged recently. Consequently, how to obtain the 3D objects from different domains is becoming a significant and challenging task. The existing approaches mainly focus on the task of retrieval from the identical dataset, which significantly constrains their implementation in real-world applications. This paper addresses the cross-domain object retrieval in an unsupervised manner, where the labels of samples from source domain are provided while the labels of samples from target domain are unknown. We propose a joint deep feature learning and visual domain adaptation method (Deep-VDA) to solve the cross-domain 3D object retrieval problem by the end-to-end learning. Specifically, benefiting from the advantages of deep learning networks, Deep-VDA employs MVCNN for deep feature extraction and domain alignment for unsupervised domain adaptation. The framework can enable the statistical and geometric shift between domains to be minimized in an unsupervised manner, which is accomplished by preserving both common and unique characteristics of each domain. Deep-VDA can improve the robustness of object features from different domains, which is important to maintain remarkable retrieval performance.

随着3D捕获设备的广泛应用，最近出现了来自不同领域的各种3D对象数据集。因此，如何从不同的领域获得3D对象正成为一项重大而具有挑战性的任务。现有方法主要集中在从相同数据集进行检索的任务上，这极大地限制了它们在实际应用中的实现。本文以一种无监督的方式解决了跨域对象检索问题，其中提供了来自源域的样本标签，而没有提供来自目标域的样本标签。我们提出了一种联合的深度特征学习和视觉域自适应方法（Deep-VDA），以通过端到端学习解决跨域3D对象检索问题。具体来说，受益于深度学习网络的优势，Deep-VDA使用MVCNN进行深度特征提取和域对齐，以实现无监督的域自适应。该框架可以使域之间的统计和几何偏移以无监督的方式最小化，这可以通过保留每个域的共同和唯一特征来实现。深度VDA可以提高来自不同域的对象特征的鲁棒性，这对于保持出色的检索性能非常重要。