Hierarchical deep semantic alignment for cross-domain 3D model retrieval

Abstract

With the development of deep learning and the widespread application of 3D modeling technology, image-based cross-domain 3D model retrieval has attracted more and more researchers’ attention. Existing methods have achieved success by aligning the feature distributions from different domains. However, previous methods just statistically align the domain-level or class-level feature distributions, leaving sample discriminability a margin to be improved for retrieval. To address this issue, this paper proposes a Hierarchical Deep Semantic Alignment Network (HDSAN) for cross-domain 3D model retrieval, which combines the proposed sample-level semantic enhancement with global domain alignment and class semantic alignment. Concretely, we adopt adversarial domain adaptation at the domain level and dynamically align the class centers of two domains at the class level. To further improve sample discriminability, we design intra-domain and cross-domain triplet center alignment to enhance the semantic representation ability at the sample level. Experiments on two commonly-used cross-domain 3D model retrieval datasets MI3DOR-1 and MI3DOR-2 demonstrate the effectiveness of the proposed method.