UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
This addresses the domain shift issue in NR-PCQA for applications like 3D graphics and virtual reality, but it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of performance degradation in no-reference point cloud quality assessment (NR-PCQA) models when there is a distribution gap between training and testing data, proposing an unsupervised progressive domain adaptation framework that improves cross-domain performance, as validated by extensive experiments.
While no-reference point cloud quality assessment (NR-PCQA) approaches have achieved significant progress over the past decade, their performance often degrades substantially when a distribution gap exists between the training (source domain) and testing (target domain) data. However, to date, limited attention has been paid to transferring NR-PCQA models across domains. To address this challenge, we propose the first unsupervised progressive domain adaptation (UPDA) framework for NR-PCQA, which introduces a two-stage coarse-to-fine alignment paradigm to address domain shifts. At the coarse-grained stage, a discrepancy-aware coarse-grained alignment method is designed to capture relative quality relationships between cross-domain samples through a novel quality-discrepancy-aware hybrid loss, circumventing the challenges of direct absolute feature alignment. At the fine-grained stage, a perception fusion fine-grained alignment approach with symmetric feature fusion is developed to identify domain-invariant features, while a conditional discriminator selectively enhances the transfer of quality-relevant features. Extensive experiments demonstrate that the proposed UPDA effectively enhances the performance of NR-PCQA methods in cross-domain scenarios, validating its practical applicability. The code is available at https://github.com/yokeno1/UPDA-main.