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Detection and typification of linear structures for dynamic visualization of 3D city models

机译：用于线性可视化3D城市模型的线性结构的检测和分类

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摘要

Cluttering is a fundamental problem in 3D city model visualization. In this paper, a novel method for removing cluttering by typification of linear building groups is proposed. This method works in static as well as dynamic visualization of 3D city models. The method starts by converting building models in higher Levels of Details (LoDs) into LoDl with ground plan and height. Then the Minimum Spanning Tree (MST) is generated according to the distance between the building ground plans. Based on the MST, linear building groups are detected for typification. The typification level of a building group is determined by its distance to the viewpoint as well as its viewing angle. Next, the selected buildings are removed and the remaining ones are adjusted in each group separately. To preserve the building features and their spatial distribution. Attributed Relational Graph (ARG) and Nested Earth Mover's Distance (NEMD) are used to evaluate the difference between the original building objects and the generalized ones. The experimental results indicate that our method can reduce the number of buildings while preserving the visual similarity of the urban areas.
机译：杂乱是3D城市模型可视化中的一个基本问题。本文提出了一种典型的线性建筑群消除杂波的新方法。此方法在3D城市模型的静态和动态可视化中均有效。该方法开始于将较高细节级别（LoD）中的建筑模型转换为具有平面图和高度的LoD1。然后根据建筑物平面图之间的距离生成最小生成树（MST）。基于MST，检测线性建筑群以进行分类。建筑物组的典型级别取决于其到视点的距离以及视角。接下来，移除选定的建筑物，并分别在每个组中调整其余建筑物。保留建筑特征及其空间分布。属性关系图（ARG）和嵌套地球移动器的距离（NEMD）用于评估原始建筑对象与广义建筑对象之间的差异。实验结果表明，我们的方法可以减少建筑物的数量，同时保持城市的视觉相似性。

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