Abstract
Image-based 3D model retrieval aims to search for 3D models according to 2D image queries, which provides a convenient way for the management of large 3D model datasets. Most of the related works put the emphasis on bridging the modality gap between 2D images and 3D models, which faces a lot of challenges due to the huge domain discrepancy. In this paper, we explicitly model and eliminate the domain-specific features of 2D images and 3D models. To alleviate the negative effect of complex background of natural images, we adopt semantic focus loss to constrain networks to learn the most semantically relevant feature representations for both 2D images and 3D models. We conduct extensive experiments on two cross-domain 3D model retrieval datasets, MI3DOR and MI3DOR-2, to show the effectiveness of the proposed method.
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