Point Cloud Quality Assessment (PCQA) methods typically predict scalar Mean Opinion Scores (MOS), which quantify overall perceptual degradation but do not reveal its causes. In contrast, human observers naturally reason in terms of specific distortions such as blur, color shifts, point density changes, missing regions, and geometric deformations. To close this gap, we introduce DAL-PCQA, a distortion-aware, language-annotated dataset for PCQA. DAL-PCQA augments benchmark point clouds with multi-level distortion severity labels, discrete quality categories, and structured natural language descriptions aligned with human perception. We define a point-cloud-specific distortion taxonomy that covers both photometric and geometric artifacts. Statistical analysis reveals characteristic degradation patterns across distortion types and quality levels. To assess the utility of these annotations, we compare zero-shot and fine-tuned multimodal models for generating perceptual quality descriptions. Experiments show that distortion-aware supervision substantially improves lexical and semantic alignment with ground-truth descriptions. By enabling interpretable, distortion-level reasoning, DAL-PCQA facilitates language-driven, explainable point cloud quality assessment. The dataset is publicly available at https://github.com/swarna96/DAL-PCQA.

翻译：摘要：点云质量评估（PCQA）方法通常预测标量平均意见分（MOS），以量化整体感知退化，但无法揭示其成因。相比之下，人类观察者会自然地依据特定失真类型进行推理，例如模糊、色彩偏移、点密度变化、区域缺失及几何形变。为弥合这一差距，我们提出了DAL-PCQA —— 一个面向PCQA的失真感知与语言标注数据集。DAL-PCQA对基准点云进行了多级失真严重程度标签、离散质量类别及与人类感知对齐的结构化自然语言描述的增强。我们定义了一套涵盖光度与几何伪影的点云特有失真分类体系。统计分析揭示了不同失真类型与质量层级间的特征退化模式。为评估这些标注的实用性，我们比较了零样本与微调多模态模型在生成感知质量描述方面的表现。实验表明，失真感知监督能显著提升词法与语义层面与真实描述的对齐程度。通过实现可解释的失真级别推理，DAL-PCQA推动了语言驱动的可解释点云质量评估。该数据集公开获取地址为：https://github.com/swarna96/DAL-PCQA。