Point Cloud Quality Assessment using 3D Saliency Maps
This addresses quality assessment for point clouds, which is important for applications like 3D modeling and virtual reality, but it is incremental as it builds on existing saliency detection ideas.
The paper tackled point cloud quality assessment by proposing a metric that uses 3D saliency maps to predict quality, showing competitive performance compared to state-of-the-art methods on four databases.
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.