利用图像质量先验提升点云质量评估泛化能力
QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment
- 引入感知质量敏感的特征对齐策略，增强排序一致性
- 跨域实验显示在多个数据集上显著提升评估准确率
- 适合需要跨场景点云质量评估的研究者使用
无参考点云质量评估（NR-PCQA）仍面临泛化难题，主要源于标注点云数据集稀缺。由于人类视觉系统（HVS）的感知质量判断独立于媒体类型，可将从图像中学习到的质量先验迁移至点云。这启发我们采用无监督域适应（UDA）方法，将标注图像中的质量相关知识迁移到无标签点云中。然而，现有基于UDA的点云质量评估方法常忽略感知质量的关键特性，如对质量排序的敏感性及质量感知特征对齐，限制了其效果。为此，本文提出一种新的质量感知域适应框架QD-PCQA。该框架包含两个核心组件：一是基于排名加权的条件对齐（RCA）策略，通过一致质量层级下的特征对齐并自适应强调误排序样本，强化对感知质量排序的意识；二是质量引导的特征增强（QFA）策略，包括质量引导的风格混合、多层扩展与双域增强模块，以增强感知特征对齐。大量跨域实验表明，QD-PCQA在NR-PCQA任务中显著提升了泛化性能。
原文摘要 · Abstract (English)
No-Reference Point Cloud Quality Assessment (NR-PCQA) still struggles with generalization, primarily due to the scarcity of annotated point cloud datasets. Since the Human Visual System (HVS) drives perceptual quality assessment independently of media types, prior knowledge on quality learned from images can be repurposed for point clouds. This insight motivates adopting Unsupervised Domain Adaptation (UDA) to transfer quality-relevant priors from labeled images to unlabeled point clouds. However, existing UDA-based PCQA methods often overlook key characteristics of perceptual quality, such as sensitivity to quality ranking and quality-aware feature alignment, thereby limiting their effectiveness. To address these issues, we propose a novel Quality-aware Domain adaptation framework for PCQA, termed QD-PCQA. The framework comprises two main components: i) a Rank-weighted Conditional Alignment (RCA) strategy that aligns features under consistent quality levels and adaptively emphasizes misranked samples to reinforce perceptual quality ranking awareness; and ii) a Quality-guided Feature Augmentation (QFA) strategy, which includes quality-guided style mixup, multi-layer extension, and dual-domain augmentation modules to augment perceptual feature alignment. Extensive cross-domain experiments demonstrate that QD-PCQA significantly improves generalization in NR-PCQA tasks.
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