基于视觉语言模型的建筑布局多粒度图自动表示方法，用于设计信息学
VLM-based automatic multi-granularity graph representation of building layouts for design informatics
- Massachusetts Institute of Technology（麻省理工学院）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出基于VLM的多粒度图自动构建方法，以147份高校图书馆平面图为案例，验证其生成的图与人工标注图一致性良好，可用于设计信息学相关任务，提升建筑全生命周期设计信息利用率。
AI中文摘要：
建筑平面图图像编码了功能空间之间丰富的关系知识，这为建筑全生命周期的设计检索、基于知识的推理和建筑信息模型（BIM）丰富化提供了基础。然而，为公共建筑自动构建任务自适应的图表示仍然具有挑战性。为解决这一缺口，我们首先为公共建筑布局定义了多粒度图级别（LoGs）。在方法上，我们提出了一种基于视觉语言模型（VLM）的自动LoG构建方法，该方法通过节点识别、边推断、文本解析和图粗化四个步骤实现。我们以全球147份高校图书馆平面图为案例，对VLM生成的表示进行了系统评估和实际任务测试。实验结果显示，VLM生成的图与人工标注图整体一致：节点匹配率≥92%，生成三层LoG图的单张平面图耗时509.3秒。中粒度图在节点级区域预测中表现最佳（宏F1值为0.647，复杂度为细粒度的65%），而粗粒度图在图级布局质量评估中最有效（斯皮尔曼相关系数ρ=0.610，复杂度为细粒度的16%）。本研究通过实现从平面图图像中可扩展、无标注地提取结构化布局信息，将平面图转换为知识表示，从而推进了设计信息学，提升了建筑全生命周期中设计信息的利用率。
英文摘要：
Architectural floorplan images encode rich relational knowledge among functional spaces, which underpins design retrieval, knowledge-based reasoning, and BIM enrichment through the building lifecycle. However, it remains challenging to automatically construct task-adaptive graph representations for public buildings. To address this gap, we first define a multi-granularity Level-of-Graphs (LoGs) for public building layouts. Methodologically, we present a Vision-Language Model (VLM)-based automatic LoG construction through node identification, edge inference, text parsing, and graph coarsening. VLM-generated representations are systematically evaluated and tested in real-world tasks, using 147 academic library floorplans worldwide as a case study. Experiments showed VLM-generated graphs were broadly consistent with human-labeled graphs (matched node ratio >= 92%; 509.3 s per floor plan for three-LoG graph generation). Meso-grained graphs yield the best node-level zone prediction (Macro F1 = 0.647, at 65% of fine-grained complexity), while coarse-grained graphs are most effective for graph-level layout quality evaluation (Spearman's \r{ho} = 0.610, at 16% of fine-grained complexity). By enabling scalable, annotation-free extraction of structured layout information from floorplan images, this study advances design informatics by converting plan images into knowledge representations, thereby enhancing the utilization of design information across the building life cycle.
