Cross-Domain 3D Model Retrieval via Visual Domain Adaption

Cross-Domain 3D Model Retrieval via Visual Domain Adaption

Anan Liu, Shu Xiang, Wenhui Li, Weizhi Nie, Yuting Su

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 828-834. https://doi.org/10.24963/ijcai.2018/115

Recent advances in 3D capturing devices and 3D modeling software have led to extensive and diverse 3D datasets, which usually have different distributions. Cross-domain 3D model retrieval is becoming an important but challenging task. However, existing works mainly focus on 3D model retrieval in a closed dataset, which seriously constrain their implementation for real applications. To address this problem, we propose a novel crossdomain 3D model retrieval method by visual domain adaptation. This method can inherit the advantage of deep learning to learn multi-view visual features in the data-driven manner for 3D model representation. Moreover, it can reduce the domain divergence by exploiting both domainshared and domain-specific features of different domains. Consequently, it can augment the discrimination of visual descriptors for cross-domain similarity measure. Extensive experiments on two popular datasets, under three designed cross-domain scenarios, demonstrate the superiority and effectiveness of the proposed method by comparing against the state-of-the-art methods. Especially, the proposed method can significantly outperform the most recent method for cross-domain 3D model retrieval and the champion of Shrec’16 Large-Scale 3D Shape Retrieval from ShapeNet Core55.
Keywords:
Machine Learning: Transfer, Adaptation, Multi-task Learning
Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation