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）旨在无需参考信息的情况下自动评估失真点云的感知质量，得益于深度神经网络的运用，该类方法已取得显著进展。然而，基于学习的NR-PCQA方法受限于标注数据的稀缺性，通常在泛化性能方面表现欠佳。为解决该问题，我们提出了一种专为点云质量评估（CoPA）设计的新型对比预训练框架，该框架能够使预训练模型从未标注数据中学习质量感知表征。为获取表征空间中的锚点，我们将不同失真类型的点云投影为图像，并随机混合其局部块以形成包含多种失真的混合图像。利用生成的锚点，我们遵循感知质量与内容及失真密切相关的核心理念，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，以有效整合来自多个视角的投影图像特征。大量实验表明，我们的方法在主流基准测试中优于现有的最先进点云质量评估方法。进一步研究证明，CoPA亦可有效提升现有基于学习的点云质量评估模型性能。