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.

翻译：无参考点云质量评估（NR-PCQA）旨在无需参考的情况下自动评估失真点云的感知质量，近年来由于深度神经网络的引入已取得显著进展。然而，基于学习的NR-PCQA方法面临标注数据稀缺问题，且普遍在泛化性能方面表现欠佳。为解决此问题，我们提出针对点云质量评估的新型对比预训练框架（CoPA），使预训练模型能够从无标注数据中学习质量感知表征。为获取表征空间中的锚点，我们将遭受不同失真的点云投影为图像，并随机混合其局部图像块以形成包含多重失真的混合图像。利用生成的锚点，我们依据"感知质量与内容及失真均密切相关"这一理念，通过质量感知对比损失约束预训练过程。此外，在模型微调阶段，我们提出语义引导的多视角融合模块，有效整合来自多个视角的投影图像特征。大量实验表明，本方法在主流基准测试中优于现有最先进的PCQA方法。进一步研究证实，CoPA还可提升现有基于学习的PCQA模型性能。