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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.07396 |
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| _version_ | 1866912372645429248 |
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| author | Wysocki, Olaf Schwab, Benedikt Biswanath, Manoj Kumar Greza, Michael Zhang, Qilin Zhu, Jingwei Froech, Thomas Heeramaglore, Medhini Hijazi, Ihab Kanna, Khaoula Pechinger, Mathias Chen, Zhaiyu Sun, Yao Segura, Alejandro Rueda Xu, Ziyang AbdelGafar, Omar Mehranfar, Mansour Yeshwanth, Chandan Liu, Yueh-Cheng Yazdi, Hadi Wang, Jiapan Auer, Stefan Anders, Katharina Bogenberger, Klaus Borrmann, Andre Dai, Angela Hoegner, Ludwig Holst, Christoph Kolbe, Thomas H. Ludwig, Ferdinand Nießner, Matthias Petzold, Frank Zhu, Xiao Xiang Jutzi, Boris |
| author_facet | Wysocki, Olaf Schwab, Benedikt Biswanath, Manoj Kumar Greza, Michael Zhang, Qilin Zhu, Jingwei Froech, Thomas Heeramaglore, Medhini Hijazi, Ihab Kanna, Khaoula Pechinger, Mathias Chen, Zhaiyu Sun, Yao Segura, Alejandro Rueda Xu, Ziyang AbdelGafar, Omar Mehranfar, Mansour Yeshwanth, Chandan Liu, Yueh-Cheng Yazdi, Hadi Wang, Jiapan Auer, Stefan Anders, Katharina Bogenberger, Klaus Borrmann, Andre Dai, Angela Hoegner, Ludwig Holst, Christoph Kolbe, Thomas H. Ludwig, Ferdinand Nießner, Matthias Petzold, Frank Zhu, Xiao Xiang Jutzi, Boris |
| contents | Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple process stages, including acquiring accurate 3D source data, reconstructing high-fidelity 3D models, maintaining models' updates, and ensuring seamless interoperability to downstream tasks. Current datasets are usually limited to one part of the processing chain, hampering comprehensive UDTs validation. To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN. This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 $m^2$ and currently 767 GB of data. By ensuring georeferenced indoor-outdoor acquisition, high accuracy, and multimodal data integration, the benchmark supports robust analysis of sensors and the development of advanced reconstruction methods. Additionally, we explore downstream tasks demonstrating the potential of TUM2TWIN, including novel view synthesis of NeRF and Gaussian Splatting, solar potential analysis, point cloud semantic segmentation, and LoD3 building reconstruction. We are convinced this contribution lays a foundation for overcoming current limitations in UDT creation, fostering new research directions and practical solutions for smarter, data-driven urban environments. The project is available under: https://tum2t.win |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset Wysocki, Olaf Schwab, Benedikt Biswanath, Manoj Kumar Greza, Michael Zhang, Qilin Zhu, Jingwei Froech, Thomas Heeramaglore, Medhini Hijazi, Ihab Kanna, Khaoula Pechinger, Mathias Chen, Zhaiyu Sun, Yao Segura, Alejandro Rueda Xu, Ziyang AbdelGafar, Omar Mehranfar, Mansour Yeshwanth, Chandan Liu, Yueh-Cheng Yazdi, Hadi Wang, Jiapan Auer, Stefan Anders, Katharina Bogenberger, Klaus Borrmann, Andre Dai, Angela Hoegner, Ludwig Holst, Christoph Kolbe, Thomas H. Ludwig, Ferdinand Nießner, Matthias Petzold, Frank Zhu, Xiao Xiang Jutzi, Boris Computer Vision and Pattern Recognition Machine Learning Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple process stages, including acquiring accurate 3D source data, reconstructing high-fidelity 3D models, maintaining models' updates, and ensuring seamless interoperability to downstream tasks. Current datasets are usually limited to one part of the processing chain, hampering comprehensive UDTs validation. To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN. This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 $m^2$ and currently 767 GB of data. By ensuring georeferenced indoor-outdoor acquisition, high accuracy, and multimodal data integration, the benchmark supports robust analysis of sensors and the development of advanced reconstruction methods. Additionally, we explore downstream tasks demonstrating the potential of TUM2TWIN, including novel view synthesis of NeRF and Gaussian Splatting, solar potential analysis, point cloud semantic segmentation, and LoD3 building reconstruction. We are convinced this contribution lays a foundation for overcoming current limitations in UDT creation, fostering new research directions and practical solutions for smarter, data-driven urban environments. The project is available under: https://tum2t.win |
| title | TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.07396 |