In this letter, a new full-reference metric is presented to assess the perceptual quality of the point clouds (PCs). The human visual system (HVS) always shows a high sensitivity to the three-dimensional (3D) edge features inherent in the PCs. With this motivation, the three-dimensional edge similarity-based model (TDESM) is proposed, which makes the first attempt to apply 3D Difference of Gaussian (3D-DOG) on point cloud quality assessment (PCQA). Specifically, the 3D edge features are captured by convolving the dual-scale 3D-DOG filters with both reference and distorted PCs. The quality scores of distorted PCs are generated by combining the 3D edge similarity measured from different scales. The experiments are conducted on four publicly available PCQA datasets, i.e., Torlig2018, M-PCCD, ICIP2020, and SJTU-PCQA. Compared with multiple state-of-the-art PCQA metrics, our proposed approach is able to be higher consistent with the subjective perception on the PCs.

中文翻译：

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通过 3D 边缘相似性测量进行点云质量评估


在这封信中，提出了一种新的全参考指标来评估点云（PC）的感知质量。人类视觉系统 (HVS) 始终对 PC 固有的三维 (3D) 边缘特征表现出高度敏感度。基于此动机，提出了基于三维边缘相似度的模型（TDESM），首次尝试将3D高斯差分（3D-DOG）应用于点云质量评估（PCQA）。具体来说，通过将双尺度 3D-DOG 滤波器与参考 PC 和失真 PC 进行卷积来捕获 3D 边缘特征。扭曲 PC 的质量分数是通过结合从不同尺度测量的 3D 边缘相似性来生成的。实验在四个公开的PCQA数据集上进行，即Torlig2018、M-PCCD、ICIP2020和SJTU-PCQA。与多种最先进的 PCQA 指标相比，我们提出的方法能够与 PC 上的主观感知更加一致。