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.