GlobalMapper: Arbitrary-Shaped Urban Layout Generation
Liu He Daniel Aliaga
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.