Point Cloud Quality Assessment using 3D Saliency Maps

• URL: http://arxiv.org/abs/2209.15475v1
• Date: Fri, 30 Sep 2022 13:59:09 GMT
• Title: Point Cloud Quality Assessment using 3D Saliency Maps
• Authors: Zhengyu Wang, Yujie Zhang, Qi Yang, Yiling Xu, Jun Sun, and Shan Liu
• Abstract summary: We propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction. 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. Finally, a saliency-based pooling strategy is proposed to generate the final quality score.
• Score: 37.290843791053256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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