论文标题
使用3D显着图评估点云质量评估
Point Cloud Quality Assessment using 3D Saliency Maps
论文作者
论文摘要
点云质量评估（PCQA）最近几天已成为一个吸引人的研究领域。考虑到显着性检测在质量评估中的重要性，我们提出了一个有效的全参考PCQA指标，该指标首次尝试利用显着性信息来促进质量预测，称为使用3D显着性图（PQSM）称为点云质量评估。具体而言，我们首先提出了一种基于投影的点云显着图生成方法，其中引入深度信息以更好地反映点云的几何特征。然后，我们构建点云本地邻域，以得出三个结构描述符，以指示几何，颜色和显着性差异。最后，提出了基于显着的合并策略来产生最终质量得分。广泛的实验是在四个独立的PCQA数据库上进行的。结果表明，与多个最新的PCQA指标相比，提出的PQSM表现出竞争性能。
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.
