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• W3170260075 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. Code and data are available at https://github.com/yi-ming-qian/roofgan." @default.
• W3170260075 created "2021-06-22" @default.
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• W3170260075 date "2021-06-01" @default.
• W3170260075 modified "2023-10-18" @default.
• W3170260075 title "Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses" @default.
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• W3170260075 doi "https://doi.org/10.1109/cvpr46437.2021.00282" @default.
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