Layered Projection-Based Quality Assessment of 3D Point Clouds

Point clouds are subject to various distortions during point cloud processing missions, any of which may lead to quality degradation. Consequently, predicting point cloud quality has attracted a lot of attention. In this paper, a layered projection-based point cloud quality metric (LP-PCQM) is proposed. We layer the distorted point cloud and its original version firstly and then extract the geometry and color features of layers. The geometry feature is obtained using the projection-based method and the color features are extracted upon RGB by using the point-based method. Finally, the LP-PCQM is a weighted linear combination of an optimal subset of these pooled geometry and color features of layers. To verify the performance of LP-PCQM, we compare it with other eight metrics including both point-based metrics and projection-based metrics on the WPC, SJTU-PCQA, and ICIP2020 database respectively. Experimental results show that the proposed metric exhibits better and more robust performance.