Abstract
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
