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A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

机译:基于图的机载LiDAR点云3D建筑物模型重构方法

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3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.
机译:3D建筑模型的重建对于环境和城市应用至关重要。机载光检测和测距(LiDAR)是一个非常有用的数据源,用于获取建筑对象的详细几何和拓扑信息。在这项研究中,我们采用了基于图的方法,该方法基于从LiDAR数据得出的建筑物轮廓的层次结构分析,来重构城市建筑模型。提出的方法首先使用基于图论的局部轮廓树方法来表示建筑物的拓扑结构,然后通过分析建筑物的拓扑关系将建筑物分为不同的部分,最后通过整合通过模型建立的所有各个模型来重建建筑物模型。二部图匹配过程。与现有方法相比,我们的方法对建筑物轮廓提供了更完整的拓扑和几何描述。我们通过将其应用于中国上海陆家嘴地区这一复杂且大型的城市场景,并使用各种类型的建筑物来评估该方法。结果表明,复杂建筑物可以成功重建,平均建模误差为0.32 m。我们提出的方法为从机载LiDAR点云重建3D建筑模型提供了有希望的解决方案。

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