No-Reference Point Cloud Quality Assessment Based on Graph Signal Variation

In real-time applications utilizing point clouds, no-reference point cloud quality assessment (NR-PCQA) methods are essential to improve the accuracy of downstream tasks. For example, in point cloud denoising, NR-PCQA results can be benchmarks for determining the optimal parameters when reference data are unavailable. This paper presents an accurate and fast NR-PCQA method based on graph signal processing. First, we propose new features derived from graph signal variation (GSV) to train a support vector regression model. These features improve the correlation with subjective scores and the robustness against inaccurate graph construction. Second, we present a point selection technique based on graph edge weights that allows us to exclude less relevant points, which results in a precise PCQA. Third, we propose a diagonal scan-line graph (DSLG) construction with a superior tradeoff between accurate and fast graph construction. Our experiments demonstrate improved accuracy and computation time compared with conventional methods with three types of open datasets.