With the rapid development of 3D vision applications based on point clouds, point cloud quality assessment(PCQA) is becoming an important research topic. However, the prior PCQA methods ignore the effect of local quality variance across different areas of the point cloud. To take an advantage of the quality distribution imbalance, we propose a no-reference point cloud quality assessment (NR-PCQA) method with local area correlation analysis capability, denoted as COPP-Net. More specifically, we split a point cloud into patches, generate texture and structure features for each patch, and fuse them into patch features to predict patch quality. Then, we gather the features of all the patches of a point cloud for correlation analysis, to obtain the correlation weights. Finally, the predicted qualities and correlation weights for all the patches are used to derive the final quality score. Experimental results show that our method outperforms the state-of-the-art benchmark NR-PCQA methods. The source code for the proposed COPP-Net can be found at https://github.com/philox12358/COPP-Net.

翻译：随着基于点云的3D视觉应用快速发展，点云质量评估（PCQA）正成为重要研究课题。然而，现有PCQA方法忽略了点云不同区域局部质量差异的影响。为利用质量分布的不平衡性，我们提出一种具有局部区域相关性分析能力的无参考点云质量评估（NR-PCQA）方法，记为COPP-Net。具体而言，我们将点云分割为块，为每个块生成纹理特征与结构特征，并将其融合为块特征以预测块质量；随后收集点云所有块的特征进行相关性分析，获得相关性权重；最后，利用所有块的预测质量与相关性权重计算最终质量分数。实验结果表明，我们的方法优于当前最先进的基准NR-PCQA方法。所提COPP-Net的源代码可在https://github.com/philox12358/COPP-Net获取。