基于语义与结构引导，生成可控制的大规模3D城市布局
Controllable Generation of Large-Scale 3D Urban Layouts with Semantic and Structural Guidance
- 融合几何与语义属性，用带权重的图结构建模城市
- 支持从2D布局扩展到真实3D建筑，输出大尺度有效模型
- 用户可直接修改语义属性来调控生成结果，适合规划与设计
城市建模对城市规划、场景合成和游戏开发至关重要。现有基于图像的方法虽能生成多样化布局，但常缺乏几何连续性和可扩展性；基于图的方法虽捕捉结构关系，却忽略地块语义。我们提出一种可控的大规模3D矢量城市布局生成框架，同时依赖几何与语义条件。通过融合几何与语义属性，引入边权重，并将建筑高度嵌入图中，该方法将2D布局扩展为真实3D结构。用户可直接修改语义属性以控制输出。实验表明，该方法能生成有效且大规模的城市模型，为数据驱动的规划与设计提供有力工具。
原文摘要 · Abstract (English)
Urban modeling is essential for city planning, scene synthesis, and gaming. Existing image-based methods generate diverse layouts but often lack geometric continuity and scalability, while graph-based methods capture structural relations yet overlook parcel semantics. We present a controllable framework for large-scale 3D vector urban layout generation, conditioned on both geometry and semantics. By fusing geometric and semantic attributes, introducing edge weights, and embedding building height in the graph, our method extends 2D layouts to realistic 3D structures. It also enables users to directly control the output by modifying semantic attributes. Experiments show that it produces valid, large-scale urban models, offering an effective tool for data-driven planning and design.
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