Surface-Sampling Based Objective Quality Assessment Metrics for Meshes

In this paper, we prove that it is feasible to perform mesh quality assessment by sampling it into point cloud. We propose a general and efficient surface-sampling based framework that can deal with various types and levels of distortions with less complexity. In this method, the original and distorted meshes are first converted into point clouds by sampling the triangle surfaces. Then, the geometry and attribute quality of the distorted mesh can be evaluated by the well-defined point cloud quality metrics. The final objective score can be obtained by fusing multiple quality metrics to get a more accurate prediction of the subjective quality. In addition, we compare the performance in terms of different sampling methods and sampling resolutions on a large public dataset, thus being able to suggest the best sampling configurations.