Controllable Generation of Large-Scale 3D Urban Layouts with Semantic and Structural Guidance
Mengyuan Niu, Xinxin Zhuo, Ruizhe Wang, Yuyue Huang, Junyan Yang, Qiao Wang
公開日: 2025/9/28
Abstract
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.