用图像生成高精度建筑热力3D模型，助力大规模节能改造
Deep Learning-based Scalable Image-to-3D Facade Parser for Generating Thermal 3D Building Models
- 直接在正射图像上建模几何体，减少透视失真
- 窗口墙比估算误差仅约5%，满足早期规划需求
- 支持街景与手持相机数据，适合城市级应用
既有建筑改造对减缓气候影响至关重要。早期改造规划需基于细节等级3（LoD3）的热力3D模型，包含窗户等特征，但其规模化、高精度识别仍是难题。本文提出可扩展的图像转3D立面解析器（SI3FP），通过计算机视觉与深度学习从图像中提取几何信息，构建LoD3热力模型。与依赖分割与投影的现有方法不同，SI3FP直接在正射图像平面建模几何基元，提供统一接口并降低透视失真。该方法支持稀疏（如谷歌街景）与密集（如手持相机）数据源。在典型瑞典住宅建筑上测试，窗口墙比估算误差约为5%，满足早期改造分析要求。该流程可推动大规模能源改造规划，并广泛应用于城市开发与规划。
原文摘要 · Abstract (English)
Renovating existing buildings is essential for climate impact. Early-phase renovation planning requires simulations based on thermal 3D models at Level of Detail (LoD) 3, which include features like windows. However, scalable and accurate identification of such features remains a challenge. This paper presents the Scalable Image-to-3D Facade Parser (SI3FP), a pipeline that generates LoD3 thermal models by extracting geometries from images using both computer vision and deep learning. Unlike existing methods relying on segmentation and projection, SI3FP directly models geometric primitives in the orthographic image plane, providing a unified interface while reducing perspective distortions. SI3FP supports both sparse (e.g., Google Street View) and dense (e.g., hand-held camera) data sources. Tested on typical Swedish residential buildings, SI3FP achieved approximately 5% error in window-to-wall ratio estimates, demonstrating sufficient accuracy for early-stage renovation analysis. The pipeline facilitates large-scale energy renovation planning and has broader applications in urban development and planning.
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