Universal Cross-Domain 3D Model Retrieval
Dan Song, Tianbao Li, Wenhui Li, Weizhi Nie, Wu Liu, An-An Liu
Abstract
Recent advances in 3D modeling technologies such as 3D scanning, reconstruction and printing produce an explosive increasing of 3D models, consequently 3D model management becomes urgent to facilitate related applications such as CAD, VR/AR and autonomous driving. However, we usually lack the labels of the recently emerging 3D models and even have no prior knowledge toward the label set relationship between new datasets and existing labeled datasets, which makes the management challenging. In this paper, a universal cross-domain 3D model retrieval framework is proposed for utilizing the labeled 2D images or 3D models to manage unlabeled 3D models with no prior knowledge about label sets. Specifically, a sample-level weighting mechanism is adopted to automatically detect the samples from the common label set for both domains. Then, both the domain-level and class-level alignments are performed for domain adaptation. Finally, the adapted features are used for 3D model retrieval. We conduct experiments on the cross-domain 3D model retrieval dataset NTU-PSB (PSB-NTU) and image-based 3D model retrieval dataset MI3DOR, and the results validate the superiority and effectiveness of the proposed method.