3-D Thermal City Reconstruction via Thermal Mapping With RGB-Mesh Guidance

Acquiring the temperatures of a 3D city model at different times and under various climatic conditions is crucial for building energy management, diagnosis, and heat distribution analysis, contributing to sustainable city development. While 3D thermal model reconstruction has been utilized for this purpose, previous methods have faced challenges with scalability and accuracy. In this paper, we propose a novel framework for 3D thermal city reconstruction with thermal mapping guided by an RGB model. Our framework consists of four steps: (1) building a high-quality reference RGB model, (2) registering thermal images to the RGB model via view synthesis and cross-modality thermal-to-visible image matching, (3) establishing pixel-level correspondence between the RGB and thermal images, and generating thermal mapping images with pixel-level alignment, and (4) converting the thermal values on the images to temperatures, and applying texture mapping to reconstruct the high-precision 3D city temperature model. The advantage of our framework is the need to build an RGB model only once, which can then guide thermal model reconstruction at any time. Our method achieves comparable geometric accuracy to the RGB model, with a C2M distance of the order of 10e-8, and superior texture quality to the direct use of thermal images, with more than 10% improvement in PSNR and SSIM, and the best performance in LPIPS.