Enriching thermal point clouds of buildings using semantic 3D building models
This work addresses the challenge of interpreting building thermal point clouds for applications like thermal analysis and deep learning model development, though it appears incremental as it builds on existing modalities.
The authors tackled the problem of lacking semantic information in thermal point clouds of buildings by proposing a workflow that enriches them with geo-position and semantics from LoD3 building models, enabling automatic co-registration and facade-detailed semantics.
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.