首个大规模多模态城市数字孪生数据集，支持城市建模与智能分析。
TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset
- 构建覆盖室内外的多源数据融合数字孪生数据集
- 包含767GB数据、32个数据子集，覆盖10万平米区域
- 适合城市规划、三维重建与多模态感知研究者使用
城市数字孪生（UDTs）已成为管理城市和整合多源异构数据的关键工具。构建数字孪生涉及多个环节的挑战，包括获取高精度3D数据、重建高保真3D模型、模型更新维护以及下游任务的无缝互操作性。现有数据集通常仅覆盖处理链中的单一环节，难以支撑数字孪生的全面验证。为此，我们推出了首个综合性多模态城市数字孪生基准数据集——TUM2TWIN。该数据集包含地理定位、语义对齐的3D模型与网络，以及多种地面、移动、航空和卫星观测数据，涵盖约10万 $m^2$ 区域，共32个数据子集，目前总数据量达767 GB。通过实现室内外一体化地理定位采集、高精度数据与多模态融合，该基准支持传感器性能评估与先进重建方法开发。我们还展示了若干下游任务应用，包括基于NeRF和Gaussian Splatting的新视角合成、太阳能潜力分析、点云语义分割及LoD3建筑重建。本工作为克服当前数字孪生构建的局限性奠定了基础，推动更智能、数据驱动的城市环境研究与发展。
原文摘要 · Abstract (English)
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
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