Reference-free Evaluation of Geometric–Textural Consistency for Photogrammetric Meshes

Photogrammetric mesh has emerged as a novel type of remote sensing data, finding wide application in diverse remote sensing scenarios, such as urban management, forestry monitoring, and agricultural surveillance. Semantic analysis of photogrammetric meshes serves as the foundation for numerous applications, in which the quality of triangular facets exerts a significant effect on the accuracy of face-wise mesh segmentation. Specifically, the inconsistency between geometric edges and textural edges often gives rise to faces with mixed semantic labels, which substantially undermines the reliability and precision of subsequent semantic analysis. Thus, the evaluation of such quality is essential for ensuring the high quality of photogrammetric meshes for semantic analysis. However, current mesh quality assessment methods predominantly focus solely on geometric and visual fidelity, lacking dedicated metrics for assessing the intrinsic consistency between geometric edges and textural boundaries. To address this limitation, we propose a reference-free approach for photogrammetric meshes based on assessing the consistency between geometric edges and texture-derived boundaries. The proposed approach projects 3D mesh faces onto the 2D texture space and compares their geometric edges with texture-derived object contours. We quantify this consistency using a novel buffer-zone–matching strategy combined with an area-weighted intersection over union (AIoU) metric. The experiments were carried on a multi-resolution data set and the public SUM Parts benchmark data set. The results indicate that the metric exhibits high sensitivity to quality variations across different levels of detail. Furthermore, a statistically significant correlation between AIoU and upper-bound semantic purity is observed, validating its reliability in reflecting intrinsic semantic segmentation potential. The proposed method also provides new insight into 3D reconstruction and mesh quality evaluation. The code is available at https://github.com/zwhoo/geometric-textural-consistency-evaluation.