用高斯点云和大模型，一键生成建筑数字孪生体
Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform
- 通过高斯点云提取建筑3D模型，结合地图平台获取位置信息
- 接入ChatGPT(4o)与Deepseek-V3/R1分析建筑视觉描述与数据
- 支持地址/邮编/坐标输入，实现云端一体化建模与可视化
城市数字孪生是利用多源数据与数据分析技术构建的虚拟城市，用于优化城市规划、基础设施管理与决策。本文提出面向单栋建筑尺度的数字孪生框架。通过连接Google Maps Platform等云地图平台，结合先进的多智能体大语言模型（ChatGPT 4o、Deepseek-V3/R1）进行数据解析，并采用基于高斯点云的网格提取流程，该框架可依据建筑地址、邮政编码或地理坐标，自动获取其3D模型、视觉描述，并实现基于大模型的数据分析与云端地图集成。
原文摘要 · Abstract (English)
Urban digital twins are virtual replicas of cities that use multi-source data and data analytics to optimize urban planning, infrastructure management, and decision-making. Towards this, we propose a framework focused on the single-building scale. By connecting to cloud mapping platforms such as Google Map Platforms APIs, by leveraging state-of-the-art multi-agent Large Language Models data analysis using ChatGPT(4o) and Deepseek-V3/R1, and by using our Gaussian Splatting-based mesh extraction pipeline, our Digital Twin Buildings framework can retrieve a building's 3D model, visual descriptions, and achieve cloud-based mapping integration with large language model-based data analytics using a building's address, postal code, or geographic coordinates.
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