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<p>Realistic road modeling is a core component of three-dimensional geographic information systems. Traditional methods usually emphasize geometric precision and rarely consider the effects of environmental conditions and natural aging. Thus, they generate visually clean and static road models that fail to dynamically represent the evolution of road states. To overcome this limitation, we propose a novel method for generating realistic 3D road models with multiple styles. The method abstracts a road structure as a top-down directed acyclic graph, decomposing geometry into semantically independent but functionally interrelated nodes. Coupled with a parameter propagation mechanism and a physically based decal library, the method enables the automatic generation of 3D road models that adhere to user-defined styles across temporal, environmental, and functional factors. We evaluated the method through experiments covering nine style categories and two user studies, validating its effectiveness in generating realistic road representations with diverse influencing factors. Compared with common generative AI and procedural methods, the method exhibits superior structural controllability and environmental interactivity. This work advances road modeling by introducing a parametric scheme that explicitly maps the semantic context into realistic representations, providing practical support for applications such as landscape design and traffic simulation.</p> Graphical Abstract <p></p>

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A Graph-based Framework for Realistic Multi-style 3D Road Modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Chen Zhang,
  • Yizhou Xie,
  • Jinming Peng,
  • Ding Ma,
  • Renzhong Guo

摘要

Realistic road modeling is a core component of three-dimensional geographic information systems. Traditional methods usually emphasize geometric precision and rarely consider the effects of environmental conditions and natural aging. Thus, they generate visually clean and static road models that fail to dynamically represent the evolution of road states. To overcome this limitation, we propose a novel method for generating realistic 3D road models with multiple styles. The method abstracts a road structure as a top-down directed acyclic graph, decomposing geometry into semantically independent but functionally interrelated nodes. Coupled with a parameter propagation mechanism and a physically based decal library, the method enables the automatic generation of 3D road models that adhere to user-defined styles across temporal, environmental, and functional factors. We evaluated the method through experiments covering nine style categories and two user studies, validating its effectiveness in generating realistic road representations with diverse influencing factors. Compared with common generative AI and procedural methods, the method exhibits superior structural controllability and environmental interactivity. This work advances road modeling by introducing a parametric scheme that explicitly maps the semantic context into realistic representations, providing practical support for applications such as landscape design and traffic simulation.

Graphical Abstract