  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
https://github.com/yi-ming-qian/roofgan .
