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.

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