ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-4/W5-2024
https://doi.org/10.5194/isprs-annals-X-4-W5-2024-341-2024
https://doi.org/10.5194/isprs-annals-X-4-W5-2024-341-2024
27 Jun 2024
 | 27 Jun 2024

Enriching Thermal Point Clouds of Buildings using Semantic 3D building Models

Jingwei Zhu, Olaf Wysocki, Christoph Holst, and Thomas H. Kolbe

Keywords: Point clouds, LoD3 building model, Co-registration, semantic information, Thermal InfraRed (TIR) images

Abstract. 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 Level of Detail (LoD)3 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: model point clouds are generated from LoD3 models, and thermal point clouds are co-registered by coarse-to-fine registration. 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.