GlobalMapper: Arbitrary-Shaped Urban Layout Generation
GlobalMapper: Arbitrary-Shaped Urban Layout Generation
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DOI：
10.1109/iccv51070.2023.00048
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Liu He;Daniel G. Aliaga
中科院分区：
文献类型：
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作者：
Liu He;Daniel G. Aliaga
建模和设计城市建筑布局对于计算机视觉、计算机图形学和城市应用具有重要意义。建筑布局由道路网络定义的城市街区中的一组建筑组成。我们观察到建筑布局是离散的结构，由多排不同形状的建筑组成，并且适合骨架化以将任意城市街区形状映射到规范形式。因此，我们提出了一种使用图注意网络构建布局生成的全自动方法。我们的方法在给定任意道路网络的情况下生成现实的城市布局，并能够基于学习的先验进行条件生成。我们的结果（包括用户研究）证明了与之前的布局生成网络相比具有卓越的性能，支持任意城市街区和不同的建筑形状，如为 28 个大城市生成布局所证明的那样。
Modeling and designing urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. A building layout consists of a set of buildings in city blocks defined by a network of roads. We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. Hence, we propose a fully automatic approach to building layout generation using graph attention networks. Our method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Our results, including user study, demonstrate superior performance as compared to prior layout generation networks, support arbitrary city block and varying building shapes as demonstrated by generating layouts for 28 large cities.