PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment

arXiv:2403.10061v1 Announce Type: new
Abstract: 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 …

abstract arxiv assessment autoencoder cloud cs.cv cs.mm data data-driven deep learning however masked autoencoder performance quality reference type