SAGS: Structure-Aware Geometry-Guided Stitching for Cost-Effective Glass Curtain Wall Inspection with Consumer UAVs

Image stitching for glass curtain walls is critical for unmanned aerial vehicle (UAV)-based infrastructure inspection, yet remains challenging due to specular reflections, transmitted background interference, and repetitive window structures. These factors introduce unreliable appearance cues and periodic correspondence ambiguity, degrading existing feature-based and learning-based matching methods. In this paper, we identify periodic matching ambiguity as a key geometric challenge and derive a sufficient condition for reliable correspondence under bounded displacement uncertainty. Based on this principle, we propose Structure-Aware Geometry-Guided Stitching (SAGS), which exploits facade structures as geometric priors. SAGS employs a fine-tuned Segment Anything Model (SAM)-based structural extractor to obtain facade masks, dense-frame geometric calibration to estimate the scale factor and geometric priors, and mask-based cross-correlation for geometry-guided alignment. A drift-aware recalibration strategy further maintains consistency during long-range UAV inspection. Experiments on a real-world glass curtain wall dataset demonstrate that SAGS achieves reliable facade panorama stitching using consumer-grade UAVs without real-time kinematic (RTK) positioning or LiDAR sensors by leveraging velocity telemetry as motion priors.