用图神经网络提升城市住宅屋顶矢量重建精度
PolyRoof: Precision Roof Polygonization in Urban Residential Building with Graph Neural Networks
- 引入注意力机制与分割损失优化模型结构
- 点位精度提升至1.33像素，线距误差降低至14.39像素
- 适合城市规划与三维建模领域的高精度需求
日益增长的详细建筑屋顶数据需求推动了自动化提取方法的发展，以克服传统方法在处理建筑几何复杂变化时的低效问题。Re:PolyWorld 结合点检测与图神经网络，为重建高细节建筑屋顶矢量数据提供了有前景的解决方案。本研究通过引入基于注意力的主干网络和额外的面积分割损失，提升了 Re:PolyWorld 在复杂城市住宅结构上的表现。尽管受制于数据集局限，实验结果显示点位置精度提高至1.33像素，线距离精度达14.39像素，重建得分显著提升至91.99%。这些结果表明，先进神经网络架构在应对复杂城市住宅几何挑战方面具有巨大潜力。
原文摘要 · Abstract (English)
The growing demand for detailed building roof data has driven the development of automated extraction methods to overcome the inefficiencies of traditional approaches, particularly in handling complex variations in building geometries. Re:PolyWorld, which integrates point detection with graph neural networks, presents a promising solution for reconstructing high-detail building roof vector data. This study enhances Re:PolyWorld's performance on complex urban residential structures by incorporating attention-based backbones and additional area segmentation loss. Despite dataset limitations, our experiments demonstrated improvements in point position accuracy (1.33 pixels) and line distance accuracy (14.39 pixels), along with a notable increase in the reconstruction score to 91.99%. These findings highlight the potential of advanced neural network architectures in addressing the challenges of complex urban residential geometries.
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