QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment

TL;DR

This paper introduces QD-PCQA, a novel domain adaptation framework that leverages image-based quality priors to improve point cloud quality assessment, addressing generalization issues in no-reference scenarios.

Contribution

The paper proposes a new quality-aware domain adaptation framework with rank-weighted feature alignment and quality-guided feature augmentation for better NR-PCQA performance.

Findings

Significant improvement in cross-domain generalization for NR-PCQA

Effective feature alignment with quality sensitivity

Enhanced perceptual quality ranking awareness

Abstract

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.