QuadWindow: A Perspective‐Aware Framework for Geometric Window Extraction From Street‐View Imagery

Rapid and reliable assessment of building damage is essential for post‐disaster response and recovery. As windows often reflect critical structural changes, their automatic extraction from street‐view images provides valuable insights for emergency assessment, urban risk modeling, and disaster database updates. Existing methods struggle to leverage the quadrilateral prior of windows due to two main issues: poor handling of perspective distortion and the lack of robust loss functions when precise vector annotations are unavailable. To overcome these challenges, we introduce QuadWindow, a framework specifically designed to handle perspective distortions through a perspective transformation sub‐network that predicts transformations from street‐view images to frontal views, significantly simplifying window extraction tasks without manual correction. Additionally, we propose a differentiable rendering loss that directly aligns predicted quadrangles with raster‐based ground truth, bypassing the need for explicit corner‐point annotations. Experimental results demonstrate that QuadWindow outperforms state‐of‐the‐art methods across five façade datasets, with an average F1‐score of 87.6% and Intersection over Union (IoU) of 78.03%, achieving 1.47% and 5.2% improvement, respectively.