Surface-Sampling Based Objective Quality Assessment Metrics for Meshes
Chunyang Fu, Xiang Zhang, Thuong Nguyen-Canh, Xiaozhong Xu, Ge Li, Shan Liu
Abstract
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.
BibTeX
@inproceedings{icassp2023_surfacesamplingb,
title = {Surface-Sampling Based Objective Quality Assessment Metrics for Meshes},
author = {Chunyang Fu and Xiang Zhang and Thuong Nguyen-Canh and Xiaozhong Xu and Ge Li and Shan Liu},
booktitle = {ICASSP 2023},
year = {2023}
}