  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.
