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FloorplanGAN: Vector residential floorplan adversarial generation
Automation in Construction ( IF 10.3 ) Pub Date : 2022-07-29 , DOI: 10.1016/j.autcon.2022.104470
Ziniu Luo, Weixin Huang

An architectural floorplan is a class of drawings that reflects the layout of rooms. The difference between a floorplan and a natural image and its dual features as both a vector graphic and a raster image makes it difficult to be generated by conventional deep neural generative models. We propose an adversarial generative framework that combines vector generation and raster discrimination for residential floorplan generation tasks. The floorplan is first generated in vector format with room areas as constraints and then discriminated in raster format visually using convolutional layers. A Differentiable Renderer connects the gap between the Vector Generator and Raster Discriminator. A self-attention mechanism is utilized to capture the interrelations of rooms in each floorplan. Experiments were conducted to demonstrate the feasibility of the proposed FloorplanGAN. In addition, we evaluated the effectiveness of generation based on diverse objective metrics and a user study. The code is available here: https://github.com/luozn15/FloorplanGAN.



中文翻译:

FloorplanGAN:矢量住宅平面图对抗生成

建筑平面图是反映房间布局的一类图纸。平面图和自然图像之间的差异及其作为矢量图形和光栅图像的双重特征使得传统的深度神经生成模型难以生成。我们提出了一个对抗性生成框架,该框架结合了矢量生成和栅格判别,用于住宅平面图生成任务。平面图首先以矢量格式生成,房间区域作为约束,然后使用卷积层以光栅格式在视觉上进行区分。可微分渲染器连接矢量生成器和光栅鉴别器之间的间隙。自注意力机制用于捕捉每个平面图中房间的相互关系。进行了实验以证明所提出的 FloorplanGAN 的可行性。此外,我们根据不同的客观指标和用户研究评估了生成的有效性。代码可在此处获得:https://github.com/luozn15/FloorplanGAN。

更新日期:2022-07-30
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