No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference, which has achieved remarkable performance due to the utilization of deep learning-based models. However, these data-driven models suffer from the scarcity of labeled data and perform unsatisfactorily in cross-dataset evaluations. To address this problem, we propose a self-supervised pre-training framework using masked autoencoders (PAME) to help the model learn useful representations without labels. Specifically, after projecting point clouds into images, our PAME employs dual-branch autoencoders, reconstructing masked patches from distorted images into the original patches within reference and distorted images. In this manner, the two branches can separately learn content-aware features and distortion-aware features from the projected images. Furthermore, in the model fine-tuning stage, the learned content-aware features serve as a guide to fuse the point cloud quality features extracted from different perspectives. Extensive experiments show that our method outperforms the state-of-the-art NR-PCQA methods on popular benchmarks in terms of prediction accuracy and generalizability.

翻译：无参考点云质量评估（NR-PCQA）旨在无参考条件下自动预测点云的感知质量，由于采用基于深度学习的模型，该方法已取得显著性能。然而，这些数据驱动模型面临标注数据稀缺的问题，在跨数据集评估中表现欠佳。为解决该问题，我们提出一种基于掩码自编码器的自监督预训练框架（PAME），帮助模型无需标注即可学习有效表征。具体而言，在将点云投影为图像后，我们的PAME采用双分支自编码器，将失真图像中的掩码补丁重建为参考图像与失真图像中的原始补丁。通过这种方式，两个分支可分别从投影图像中学习内容感知特征和失真感知特征。此外，在模型微调阶段，学习到的内容感知特征作为指引，融合从不同视角提取的点云质量特征。大量实验表明，我们的方法在主流基准测试中的预测精度与泛化性均优于现有最优的NR-PCQA方法。