Point cloud quality assessment (PCQA) has become an appealing research field
in recent days. Considering the importance of saliency detection in quality
assessment, we propose an effective full-reference PCQA metric which makes the
first attempt to utilize the saliency information to facilitate quality
prediction, called point cloud quality assessment using 3D saliency maps
(PQSM). Specifically, we first propose a projection-based point cloud saliency
map generation method, in which depth information is introduced to better
reflect the geometric characteristics of point clouds. Then, we construct point
cloud local neighborhoods to derive three structural descriptors to indicate
the geometry, color and saliency discrepancies. Finally, a saliency-based
pooling strategy is proposed to generate the final quality score. Extensive
experiments are performed on four independent PCQA databases. The results
demonstrate that the proposed PQSM shows competitive performances compared to
multiple state-of-the-art PCQA metrics