EPES: Point Cloud Quality Modeling Using Elastic Potential Energy Similarity

Accurately predicting point cloud quality plays an important role in human vision tasks. This paper presents an effective and robust objective point cloud quality assessment model called elastic potential energy similarity (EPES). Motivated by the knowledge on point cloud distortion, EPES first expresses a point cloud as a collection of spatially scattered points. A set of origins are then deployed and the scattered points are assumed to be connected to the nearest origin using springs. Imposing external forces can move the scattered points to specific locations such that the resulting point clouds would exhibit desired characteristics. At the same time, this process will store elastic potential energies in the springs. Therefore, through comparing the elastic potential energies kept in the springs of the reference and distorted point clouds, we are able to quantify the influence of distortion on the point cloud quality. The proposed quality assessment model is evaluated on three fairly large databases, SJTU-PCQA, CPCQA, and LSPCQA. Experimental results show that EPES is superior to the state-of-the-art metrics. Ablation studies demonstrate that the developed EPES is robust to variations in the model parameter settings.