Enriching thermal point clouds of buildings using semantic 3D building models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913463201169408 |
|---|---|
| author | Zhu, Jingwei Wysocki, Olaf Holst, Christoph Kolbe, Thomas H. |
| author_facet | Zhu, Jingwei Wysocki, Olaf Holst, Christoph Kolbe, Thomas H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enriching thermal point clouds of buildings using semantic 3D building models Zhu, Jingwei Wysocki, Olaf Holst, Christoph Kolbe, Thomas H. Computer Vision and Pattern Recognition 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. |
| title | Enriching thermal point clouds of buildings using semantic 3D building models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.21436 |