  Thermal point clouds integrate thermal radiation and laser point clouds
effectively. However, the semantic information for the interpretation of
building thermal point clouds can hardly be precisely inferred. Transferring
the semantics encapsulated in 3D building models at LoD3 has a potential to
fill this gap. In this work, we propose a workflow enriching thermal point
clouds with the geo-position and semantics of LoD3 building models, which
utilizes features of both modalities: The proposed method can automatically
co-register the point clouds from different sources and enrich the thermal
point cloud in facade-detailed semantics. The enriched thermal point cloud
supports thermal analysis and can facilitate the development of currently
scarce deep learning models operating directly on thermal point clouds.
