This paper presents Roof-GAN, a novel generative adversarial network that
generates structured geometry of residential roof structures as a set of roof
primitives and their relationships. Given the number of primitives, the
generator produces a structured roof model as a graph, which consists of 1)
primitive geometry as raster images at each node, encoding facet segmentation
and angles; 2) inter-primitive colinear/coplanar relationships at each edge;
and 3) primitive geometry in a vector format at each node, generated by a novel
differentiable vectorizer while enforcing the relationships. The discriminator
is trained to assess the primitive raster geometry, the primitive
relationships, and the primitive vector geometry in a fully end-to-end
architecture. Qualitative and quantitative evaluations demonstrate the
effectiveness of our approach in generating diverse and realistic roof models
over the competing methods with a novel metric proposed in this paper for the
task of structured geometry generation. Code and data are available at
this https URL .

本文介绍了 Roof-GAN，一种新颖的生成对抗网络，它能够生成住宅屋顶结构的结构几何体系，作为一组屋顶基元及其关系。生成器将基元的数量转换成图形结构，生成一种有结构的屋顶模型，并在完全端到端的体系结构中使用判别器来评估基元栅格几何、基元关系和基元向量几何。我们提出了一种新的度量标准，证明了我们的方法在生成多种多样和逼真的屋顶模型方面的有效性。