Static meshes with texture maps have attracted considerable attention in both industrial manufacturing and academic research, leading to an urgent requirement for effective and robust objective quality evaluation. However, current model-based static mesh quality metrics have obvious limitations: most of them only consider geometry information, while color information is ignored, and they have strict constraints for the meshes' geometrical topology. Other metrics, such as image-based and point-based metrics, are easily influenced by the prepossessing algorithms, e.g., projection and sampling, hampering their ability to perform at their best. In this paper, we propose Geodesic Patch Similarity (GeodesicPSIM), a novel model-based metric to accurately predict human perception quality for static meshes. After selecting a group keypoints, 1-hop geodesic patches are constructed based on both the reference and distorted meshes cleaned by an effective mesh cleaning algorithm. A two-step patch cropping algorithm and a patch texture mapping module refine the size of 1-hop geodesic patches and build the relationship between the mesh geometry and color information, resulting in the generation of 1-hop textured geodesic patches. Three types of features are extracted to quantify the distortion: patch color smoothness, patch discrete mean curvature, and patch pixel color average and variance. To the best of our knowledge, GeodesicPSIM is the first model-based metric especially designed for static meshes with texture maps. GeodesicPSIM provides state-of-the-art performance in comparison with image-based, point-based, and video-based metrics on a newly created and challenging database. We also prove the robustness of GeodesicPSIM by introducing different settings of hyperparameters. Ablation studies also exhibit the effectiveness of three proposed features and the patch cropping algorithm.

翻译：带纹理贴图的静态网格在工业制造和学术研究中受到广泛关注，亟需有效且鲁棒的客观质量评估方法。然而，现有基于模型的静态网格质量指标存在明显局限性：多数仅考虑几何信息而忽略颜色信息，且对网格的几何拓扑结构有严格约束。其他指标（如图像基指标和点基指标）易受预处理算法（如投影和采样）影响，难以发挥最佳性能。本文提出GeodesicPSIM（测地线贴片相似性）——一种新型基于模型的指标，可精确预测静态网格的人类感知质量。在选取一组关键点后，基于经有效网格清洗算法处理后的参考网格与失真网格构建1-hop测地线贴片。通过两步贴片裁剪算法和贴片纹理映射模块，优化1-hop测地线贴片尺寸并建立网格几何信息与颜色信息的关联，最终生成1-hop纹理测地线贴片。我们提取三类特征量化失真：贴片颜色平滑度、贴片离散平均曲率及贴片像素颜色均值与方差。据我们所知，GeodesicPSIM是首个专为带纹理贴图静态网格设计的基于模型的指标。在新构建且具有挑战性的数据库上，与图像基、点基及视频基指标相比，GeodesicPSIM展现出最优性能。通过引入不同超参数设置，我们验证了其鲁棒性。消融研究亦证明了所提出的三类特征及贴片裁剪算法的有效性。