Progressive Contrastive Label Optimization for Source-Free Universal 3D Model Retrieval

Unsupervised Cross-Domain 3D Model Retrieval (UCD3DMR) has emerged as an effective tool for managing 3D model data recently. However, existing UCD3DMR algorithms typically demand accessibility to source data and cross-domain label consistency, limiting their deployment in real-world industrial scenarios. Therefore, we relax the two demanding constraints and explore to address a newly challenging task, source-free universal 3D model retrieval (SFU3DMR). However, the inaccessibility to source data results in significant label noise in target pseudo-labels, while cross-domain label inconsistency introduces interference from target-private models, presenting tremendous challenges to model transfer. To address these challenges, we propose a novel SFU3DMR algorithm, Progressive Contrastive Label Optimization (PCLO). Specifically, we introduce the Neighbor-based Soft Label Optimization (NSLO) strategy, which refines target pseudo-labels based on the pseudo-label confidence of their nearest neighbors. Additionally, we design the Adaptive Hybrid Label Optimization (AHLO) strategy, which conducts positive label optimization to maximize label semantics for target-common models and executes negative label optimization to minimize label noise for target-private models. Experimental results confirm that the combined NSLO and AHLO strategies effectively refine the target pseudo-labels, and our PCLO achieves state-of-the-art performance for SFU3DMR on two well-established cross-domain benchmarks (MI3DOR and NTU/PSB).