MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909099563679744 |
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| author | Wysocki, Olaf Hoegner, Ludwig Stilla, Uwe |
| author_facet | Wysocki, Olaf Hoegner, Ludwig Stilla, Uwe |
| contents | Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper. Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals. We believe that our method can foster development of LoD3 reconstruction algorithms and subsequently enable their wider adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06288 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models Wysocki, Olaf Hoegner, Ludwig Stilla, Uwe Computer Vision and Pattern Recognition Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper. Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals. We believe that our method can foster development of LoD3 reconstruction algorithms and subsequently enable their wider adoption. |
| title | MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.06288 |