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.

翻译：尽管无参考点云质量评估（NR-PCQA）方法在过去十年中取得了显著进展，但当训练（源域）与测试（目标域）数据之间存在分布差异时，其性能往往会大幅下降。然而，迄今为止，针对NR-PCQA模型跨域迁移的研究仍十分有限。为应对这一挑战，我们提出了首个面向NR-PCQA的无监督渐进式域适应（UPDA）框架，该框架引入了一种由粗到精的两阶段对齐范式以应对域偏移问题。在粗粒度阶段，我们设计了一种差异感知的粗粒度对齐方法，通过一种新颖的质量差异感知混合损失来捕捉跨域样本间的相对质量关系，从而规避直接绝对特征对齐的困难。在细粒度阶段，我们开发了一种基于对称特征融合的感知融合细粒度对齐方法，以识别域不变特征，同时利用条件判别器选择性地增强质量相关特征的迁移。大量实验表明，所提出的UPDA方法能有效提升NR-PCQA方法在跨域场景下的性能，验证了其实际适用性。代码已发布于 https://github.com/yokeno1/UPDA-main。