QD-PCQA：面向点云质量评估的质量感知域自适应
QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment
将图像质量评估的预训练先验通过无监督域自适应迁移到点云质量评估，其中排序加权条件对齐和双域增强策略可复用于其他跨模态质量评估任务。
原文依据 This insight motivates adopting Unsupervised Domain Adaptation (UDA) to transfer quality-relevant priors from labeled images to unlabeled point clouds.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无参考点云质量评估（NR-PCQA）在泛化性方面仍存在困难，主要原因是标注点云数据集的稀缺。由于人类视觉系统（HVS）驱动感知质量评估且与媒体类型无关，从图像中学到的质量先验知识可以重新用于点云。这一见解促使采用无监督域自适应（UDA）将有标签图像中的质量相关先验迁移到无标签点云。然而，现有的基于UDA的PCQA方法往往忽略了感知质量的关键特性，例如对质量排序的敏感性和质量感知的特征对齐，从而限制了其有效性。为解决这些问题，我们提出了一种新颖的面向PCQA的质量感知域自适应框架，称为QD-PCQA。该框架包含两个主要组件：i）排序加权条件对齐（RCA）策略，该策略在一致的质量水平下对齐特征，并自适应地强调排序错误的样本以增强感知质量排序意识；ii）质量引导特征增强（QFA）策略，包括质量引导风格混合、多层扩展和双域增强模块，以增强感知特征对齐。大量跨域实验表明，QD-PCQA显著提高了NR-PCQA任务的泛化性。
英文原文摘要 1389 字符
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.