Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses
This work addresses the challenge of generating realistic and structured 3D roof models for architects and urban planners, offering an incremental improvement in geometric modeling.
This paper introduces Roof-GAN, a generative adversarial network designed to produce structured roof geometries for residential houses. It generates roof models as graphs, including primitive geometry (raster and vector) and inter-primitive relationships, outperforming competing methods in diversity and realism.
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 .