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

• URL: http://arxiv.org/abs/2403.10061v1
• Date: Fri, 15 Mar 2024 07:01:33 GMT
• Title: PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment
• Authors: Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang, Yiling Xu, Shan Liu,
• Abstract summary: No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference. We propose a self-supervised pre-training framework using masked autoencoders (PAME) to help the model learn useful representations without labels. Our method outperforms the state-of-the-art NR-PCQA methods on popular benchmarks in terms of prediction accuracy and generalizability.
• Score: 34.256276774430575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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