用图注意力模型分析街景建筑立面，自动估算楼层数并解释元素分布。
GATA2Floor: Graph attention for floor counting in street-view facades
- 将窗户门洞建为带垂直约束的图结构，用GATv2推理楼层
- 通过可学习查询软分配元素到楼层，准确率达89.2%
- 无需标注数据，靠自监督特征+视觉语言评分实现无监督训练
从街景图像中自动化分析建筑立面在城市分析、能源评估和应急规划中具有巨大潜力。然而，这需要对空间排列的构件进行推理，而不仅仅是孤立检测。本文将每栋建筑立面建模为基于窗户/门检测的图结构，并在边中引入垂直先验。同时提出GATA2Floor模型，该模型基于多头GATv2，预测建筑整体楼层数，并通过可学习的交叉注意力查询，将构件软分配至隐式楼层槽位，从而生成可解释输出并增强对不规则设计的鲁棒性。为缓解标注数据缺乏问题，我们证明所提出的基于图的推理可无需标注数据应用，方法基于轻量级无监督提议机制，结合自监督特征与视觉-语言评分。本方法展示了图注意力驱动关系推理在立面理解中的价值。
原文摘要 · Abstract (English)
Automated analysis of building facades from street-level imagery has great potential for urban analytics, energy assessment, and emergency planning. However, it requires reasoning over spatially arranged elements rather than solely isolated detections. In this work, we model each facade as a graph over window/door detections with a vertical prior on edges. Additionally, we introduce GATA2Floor, a multi-head Graph Attention v2 (GATv2) based model that predicts the global floor count of a building and, via learnable cross-attention queries, softly assigns elements to latent floor slots, yielding interpretable outputs and robustness to irregular designs. To mitigate the lack of labeled datasets, we demonstrate that the proposed graph-based reasoning can be applied without annotations by leveraging a lightweight label-free proposal mechanism based on self-supervised features and vision-language scoring. Our approach demonstrates the value of graph-attention-based relational reasoning for facade understanding.
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