  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.
