  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.
