测绘学报 ›› 2024, Vol. 53 ›› Issue (7): 1355-1370.doi: 10.11947/j.AGCS.2024.20230482

• 摄影测量与遥感 • 上一篇    下一篇

面向室内空间智能的三维场景图表达与应用

汤圣君1,2(), 杜思齐2, 王伟玺1,2(), 郭仁忠1,2   

  1. 1.深圳大学亚热带建筑与城市科学全国重点实验室,广东 深圳 518061
    2.深圳大学建筑与城市规划学院,广东 深圳 518061
  • 收稿日期:2023-10-20 发布日期:2024-08-12
  • 通讯作者: 王伟玺 E-mail:shengjuntang@szu.edu.cn;wangwx@szu.edu.cn
  • 作者简介:汤圣君(1991—),男,博士,副研究员,主要研究方向为城市三维要素结构化重建、多传感器融合测图等。E-mail:shengjuntang@szu.edu.cn
  • 基金资助:
    广东省自然科学基金(2024A1515030061);深圳市科技计划项目(KJZD20230923115508017);亚热带建筑与城市科学全国重点实验室自主研究课题(2023ZB18)

3D scene graph representation and application for intelligent indoor spaces

Shengjun TANG1,2(), Siqi DU2, Weixi WANG1,2(), Renzhong GUO1,2   

  1. 1.State Key Laboratory of Subtropical Building and Urban Science, Shenzhen University, Shenzhen 518061, China
    2.School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518061, China
  • Received:2023-10-20 Published:2024-08-12
  • Contact: Weixi WANG E-mail:shengjuntang@szu.edu.cn;wangwx@szu.edu.cn
  • About author:TANG Shengjun (1991—), male, PhD, associate researcher, majors in urban 3D element structured reconstruction and multi-sensor fusion mapping. E-mail: shengjuntang@szu.edu.cn
  • Supported by:
    The Natural Science Foundation of Guangdong Province(2024A1515030061);Research Project of Shenzhen Science and Technology Innovation Committee(KJZD20230923115508017);The Research Project of State Key Laboratory of Subtropical Building and Urban Science(2023ZB18)

摘要:

现有室内三维场景表达聚焦于对象化的描述方法,其要素表达还停留在对象级语义理解层面,欠缺对室内场景复杂关系信息的显示表达。面向室内空间智能任务需求,亟需一个能够完整、准确描述室内要素几何、语义及关系,且具备语义检索和分析推理能力的结构化模型支撑。基于三维场景图基础理论,本文创新性地提出面向室内空间智能的三维场景图表达模型,系统性介绍了室内三维场景图要素层级化组织、几何表达、语义描述、关系描述方法,建立了一个可统一描述室内要素几何、语义及关系的室内三维场景图概念模型。同时,该图模型可以与现有的三维场景表达方法进行融合表达,具有良好的数据兼容性。最终,本文基于公开的IFC模型构建了完整且具有多层级关系信息的三维场景图模型,并且结合大语言模型通过复杂场景检索、拓扑分析等应用对该模型的应用能力、潜力及局限性进行了系统性探讨和分析。结果表明,室内三维场景图模型具备复杂计算和分析能力,可直接与大语言模型集成,通过简单的自然语言提示实现复杂的场景分析应用。

关键词: 室内建模, 图模型, IFC, CityGML, 大语言模型

Abstract:

Existing methods for indoor 3D scene representation focus on object-oriented descriptions, with element representations limited to object-level semantic understanding. These methods lack the ability to express complex relational information within indoor scenes. Addressing the demands of intelligent indoor space tasks, there is a critical need for a structured model that can comprehensively and accurately describe the geometry, semantics, and relationships of indoor elements, while also supporting semantic retrieval and analytical reasoning. Based on the fundamental theory of 3D scene graphs, this paper innovatively proposes a 3D scene graph representation model tailored for intelligent indoor spaces. It systematically introduces the hierarchical organization, geometric representation, semantic description, and relational description methods of indoor 3D scene graphs. A conceptual model is established that uniformly describes the geometry, semantics, and relationships of indoor elements. Additionally, this graph model is compatible with existing 3D scene representation methods, ensuring good data compatibility. Finally, a comprehensive multi-level relational 3D scene graph model is constructed based on the publicly available IFC model. This model's application capabilities, potential, and limitations are systematically explored and analyzed through applications such as complex scene retrieval and topological analysis, in conjunction with large language models. The results demonstrate that the indoor 3D scene graph model possesses complex computation and analysis capabilities, can be directly integrated with large language models, and enables complex scene analysis applications through simple natural language prompts.

Key words: indoor modeling, graph model, IFC, CityGML, large language model

中图分类号: