DAL-PCQA: Enabling Distortion-Level and Language-Driven Reasoning for Point Cloud Quality Assessment

2026-06-05

AI Summary

This work addresses the limited interpretability of existing point cloud quality assessment (PCQA) methods, which typically predict only overall subjective scores without elucidating the underlying perceptual degradations. To bridge this gap, we introduce a novel dataset featuring the first distortion taxonomy specifically designed for point clouds, accompanied by multi-level distortion severity labels, discrete quality categories, and structured natural language descriptions. Leveraging this dataset, we conduct zero-shot and fine-tuned experiments using multimodal models augmented with human-perception-aligned natural language generation. Our results demonstrate that incorporating distortion-aware supervision significantly enhances the lexical and semantic alignment between generated descriptions and human annotations, thereby advancing interpretable PCQA.

Abstract

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.

Problem

• Point Cloud Quality Assessment
• Distortion-Level Reasoning
• Language-Driven Evaluation
• Perceptual Quality
• Interpretable Assessment

Innovation

• distortion-aware
• language-driven
• point cloud quality assessment
• multimodal reasoning
• explainable AI