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Automatic conversion of IFC datasets to geometrically and semantically correct CityGML LOD3 buildings

机译:将IFC数据集自动转换为几何和语义正确的CityGML LOD3建筑物

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摘要

Although the international standard CityGML has five levels of detail (LODs), the vast majority of available models are the coarse ones (up to LOD2, i.e. block-shaped buildings with roofs). LOD3 and LOD4 models, which contain architectural details such as balconies, windows and rooms, rarely exist because, unlike coarser LODs, their construction requires several datasets that must be acquired with different technologies, and often extensive manual work is needed. In this article we investigate an alternative to obtaining CityGML LOD3 models: the automatic conversion from already existing architectural models (stored in the IFC format). Existing conversion algorithms mostly focus on the semantic mappings and convert all the geometries, which yields CityGML models having poor usability in practice (spatial analysis, for instance, is not possible). We present a conversion algorithm that accurately applies the correct semantics from IFC models and that constructs valid CityGML LOD3 buildings by performing a series of geometric operations in 3D. We have implemented our algorithm and we demonstrate its effectiveness with several real-world datasets. We also propose specific improvements to both standards to foster their integration in the future.
机译:尽管国际标准的CityGML具有五个详细级别(LOD),但可用的绝大部分模型是粗糙的模型(不超过LOD2,即带有屋顶的块状建筑物)。包含阳台,窗户和房间等建筑细节的LOD3和LOD4模型很少出现,因为与粗糙的LOD不同,它们的构造需要使用不同的技术来获取多个数据集,并且通常需要大量的人工工作。在本文中,我们研究了获取CityGML LOD3模型的一种替代方法:从已经存在的体系结构模型(以IFC格式存储)进行自动转换。现有的转换算法主要集中在语义映射上并转换所有几何形状,这导致CityGML模型在实践中实用性较差(例如,不可能进行空间分析)。我们提出了一种转换算法,该算法可以准确地应用IFC模型中的正确语义,并通过在3D中执行一系列几何运算来构造有效的CityGML LOD3建筑物。我们已经实现了我们的算法,并通过几个实际数据集证明了其有效性。我们还建议对这两个标准进行特定的改进,以促进将来的集成。

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