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Separating Domain-Private Classes for Universal Unsupervised Cross-Domain 3D Model Retrieval
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AI
DOI：10.1109/TMM.2025.3623513.png)
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Abstract
En 中文
Unsupervised cross-domain 3D model retrieval (UCD3DMR), which enables the transfer of knowledge from existing labeled data to new unlabeled 3D models, has emerged as an effective tool for managing 3D models recently. However, most existing UCD3DMR methods focus on closed-set scenarios, requiring the source and target domains to share identical categories, which is idealized and impractical. Consequently, we explore a more practical yet demanding task known as universal unsupervised cross-domain 3D model retrieval. This task faces significant data distribution discrepancies and uncertain category overlap across domains, posing significant challenges for cross-domain adaptation. To address these challenges, we introduce an innovative universal UCD3DMR method named Separate Domain-Private Classes (SDPC), which integrates a Weighted Alignment Mechanism (WAM) and a Domain-private Separation Mechanism (DSM). Specifically, we formulate a couple of transferability criteria to select domain-common class samples for cross-domain alignment. Additionally, we design a couple of separation losses to mitigate interference from domain-private class samples. The transferability criteria and separation losses can mutually enhance the cross-domain alignment, leading to further cross-domain retrieval performance improvement. Extensive experiments on two well-established cross-domain 3D model datasets (MI3DOR and NTU/PSB), validate and highlight the superior performance of our SDPC.
Keywords:
Universal domain adaptation
unsupervised cross-domain retrieval
3D model management
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