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Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
March 18, 2024, 4:44 a.m. | Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang, Yiling Xu, Jenq-Neng Hwang, Xiaozhong Xu, Shan Liu
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv assessment cloud cs.cv cs.mm data fusion however improvements networks neural networks pre-training quality reference training type view
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