No-reference point cloud quality assessment (NR-PCQA) aims to automatically
evaluate the perceptual quality of distorted point clouds without available
reference, which have achieved tremendous improvements due to the utilization
of deep neural networks. However, learning-based NR-PCQA methods suffer from
the scarcity of labeled data and usually perform suboptimally in terms of
generalization. To solve the problem, we propose a novel contrastive
pre-training framework tailored for PCQA (CoPA), which enables the pre-trained
model to learn quality-aware representations from unlabeled data. To obtain
anchors in the representation space, we project point clouds with different
distortions into images and randomly mix their local patches to form mixed
images with multiple distortions. Utilizing the generated anchors, we constrain
the pre-training process via a quality-aware contrastive loss following the
philosophy that perceptual quality is closely related to both content and
distortion. Furthermore, in the model fine-tuning stage, we propose a
semantic-guided multi-view fusion module to effectively integrate the features
of projected images from multiple perspectives. Extensive experiments show that
our method outperforms the state-of-the-art PCQA methods on popular benchmarks.
Further investigations demonstrate that CoPA can also benefit existing
learning-based PCQA models.

提出了一种针对点云质量评估的新型对比度预训练框架（CoPA），该框架通过无监督数据学习质量感知表示，并利用生成的锚点通过质量感知对比度损失限制预训练过程，进一步采用语义引导多视角融合模块在模型微调阶段有效地整合多个视角的投影图像特征，实验证明该方法在流行基准上优于现有的点云质量评估方法，并能够提升现有的学习型点云质量评估模型。