Point Cloud Quality Assessment via 3D Edge Similarity Measurement
Zian Lu, Hailiang Huang, Huanqiang Zeng, Junhui Hou, Kai‐Kuang Ma
Abstract
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.