Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to user-ends for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experimental results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests.

翻译：许多密集的三维点云被利用来代表视觉物体,而不是传统图像或视频。为了评价各种点云的感知质量,我们在本信中提议根据内容导向的“弹性预测”(RR-CAP),对点云提出新的、高效的降低参考质量指标。具体地说,我们第一次试图将参考和扭曲点云简化成预测的显要地图,并进行下取样作业。通过这一过程,我们处理将大容量原始点云传送到用户端进行质量评估的问题。然后,根据人类视觉系统(HVS)的特性,通过将内容导向的相似性和统计相关性测量结合起来,得出扭曲点云的客观质量分数。最后,对STU-PCQA和WPC数据库进行了广泛的实验。实验结果表明,我们提议的算法超越了现有的低参考和不参照质量测量,并大大缩小了状态全面参考质量评估方法之间的性差。此外,我们通过进行断层测试,显示了每个拟议技术组成部分的性能差异。