论文标题
房屋：用于图形约束的房屋布局一代的关系生成的对抗网络
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
论文作者
论文摘要
本文提出了一个新颖的图形受限的生成对抗网络，其生成器和歧视器建立在关系体系结构的基础上。主要思想是将约束对其关系网络的图形结构进行编码。我们已经证明了新的房屋布局生成问题的拟议架构，其任务是将建筑约束作为图形（即带有空间邻接的房间的数量和类型），并产生一组与轴对齐的房间的边界盒。我们用三个指标来衡量生成的房屋布局的质量：现实主义，多样性和与输入图约束的兼容性。我们的定性和定量评估超过117,000个真实的地板图像表明，所提出的方法的表现优于现有方法和基准。我们将公开共享我们所有的代码和数据。
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.
