Joint deep feature learning and unsupervised visual domain adaptation for cross-domain 3D object retrieval

Abstract

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.