Point Cloud Quality Assessment using 3D Saliency Maps

TL;DR

This paper introduces PQSM, a novel full-reference point cloud quality assessment method that leverages 3D saliency maps and structural descriptors to improve quality prediction accuracy.

Contribution

It is the first to incorporate 3D saliency maps into point cloud quality assessment, enhancing geometric and perceptual quality evaluation.

Findings

PQSM achieves competitive performance on four PCQA databases.

The saliency-based pooling improves quality score accuracy.

Incorporating depth information enhances saliency map relevance.

Abstract

Point cloud quality assessment (PCQA) has become an appealing research field in recent days. Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, called point cloud quality assessment using 3D saliency maps (PQSM). Specifically, we first propose a projection-based point cloud saliency map generation method, in which depth information is introduced to better reflect the geometric characteristics of point clouds. Then, we construct point cloud local neighborhoods to derive three structural descriptors to indicate the geometry, color and saliency discrepancies. Finally, a saliency-based pooling strategy is proposed to generate the final quality score. Extensive experiments are performed on four independent PCQA databases. The results demonstrate that the proposed PQSM shows competitive performances compared to multiple state-of-the-art PCQA metrics.