Automatic generation of thermal point clouds from TIR images and LiDAR point clouds

3D thermal models are associated with building inspection and energy efficiency evaluation. Fusing Thermal infrared (TIR) images with MLS (Mobile Laser scanning) point clouds enables the generation of thermal point clouds, which combine detailed geometric data with thermal attributes at each 3D point. RGB images are typically used to recon-
struct a 3D point cloud and apply thermal textures to the model. Therefore, the generated thermal point cloud heavily relies on accurate RGB reconstruction and scale estimation. In this contribution, we introduce a novel image-feature alignment method to directly co-register TIR images with MLS point clouds. The intensity images are generated
from the point clouds, and corresponding feature points are matched with the TIR images. With the estimated corresponding points, the pose can be calculated, and the thermal textures are projected onto the MLS point clouds for thermal point cloud generation. Our method achieves results comparable to manual labeling with a projection error of RMSE 3.4 pixels, offering an efficient and reliable solution to generate 3D thermal models for building energy evaluations.