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Context-based urban terrain reconstruction from uav-videos for geoinformation applications

机译:从无人机视频中基于上下文的城市地形重建,用于地理信息应用

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摘要

Urban terrain reconstruction has many applications in areas of civil engineering, urban planning, surveillance and defense research. Therefore the needs of covering ad-hoc demand and performing a close-range urban terrain reconstruction with miniaturized and relatively inexpensive sensor platforms are constantly growing. Using (miniaturized) unmanned aerial vehicles, (M) UAVs, represents one of the most attractive alternatives to conventional large-scale aerial imagery. We cover in this paper a four-step procedure of obtaining georeferenced 3D urban models from video sequences. The four steps of the procedure - orientation, dense reconstruction, urban terrain modeling and geo-referencing - are robust, straight-forward, and nearly fully-automatic. The two last steps - namely, urban terrain modeling from almost-nadir videos and co-registration of models - represent the main contribution of this work and will therefore be covered with more detail. The essential substeps of the third step include digital terrain model (DTM) extraction, segregation of buildings from vegetation, as well as instantiation of building and tree models. The last step is subdivided into quasi-intrasensorial registration of Euclidean reconstructions and intersensorial registration with a geo-referenced orthophoto. Finally, we present reconstruction results from a real data-set and outline ideas for future work.
机译:城市地形重建在土木工程,城市规划,监视和国防研究领域具有许多应用。因此,满足临时需求和使用小型化且相对便宜的传感器平台执行近距离城市地形重建的需求正在不断增长。使用(微型)无人飞行器(M)无人机代表了传统大规模航空影像的最有吸引力的替代方案之一。我们在本文中介绍了从视频序列中获取地理参考3D城市模型的四步过程。该过程的四个步骤-定位,密集的重建,城市地形建模和地理参考-十分健壮,简单,几乎是全自动的。最后两个步骤-从几乎最低点的视频进行城市地形建模和模型的共同注册-代表了这项工作的主要贡献,因此将进行更详细的介绍。第三步的基本子步骤包括数字地形模型(DTM)提取,建筑物与植被的分离以及建筑物和树木模型的实例化。最后一步分为欧氏重建的准感官内配准和地理参考正射影像的感官内配准。最后,我们展示了真实数据集的重建结果并概述了未来工作的思路。

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