Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses

• URL: http://arxiv.org/abs/2012.09340v1
• Date: Thu, 17 Dec 2020 00:47:57 GMT
• Title: Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses
• Authors: Yiming Qian, Hao Zhang, Yasutaka Furukawa
• Abstract summary: Roof-GAN is a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. The generator produces a structured roof model as a graph, which consists of 1) primitive geometry as images at each node, 2) inter-primitive colinear/coplanar relationships at each edge, and 3) primitive geometry in a vector format at each node. The discriminator is trained to assess the primitive geometry, the primitive relationships, and the primitive vector geometry in a fully end-to-end architecture.
• Score: 37.6686237027665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We will share our code and data.

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