QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment
Guohua Zhang, Jian Jin, Meiqin Liu, Chao Yao, and Weisi Lin

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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Face recognition and analysis · Generative Adversarial Networks and Image Synthesis
