Abstract:

Floorplan design is an important step of house design. However， the existing automatic floorplan design methods lack the common constraints of considering user requirements and building boundaries. Thus， these methods suffer from unreasonable layout problems such as missing corners of generated room， severe occlusion between rooms and room getting out of the boundary. In order to solve the above problems， a building floorplan GBC-GAN （Graph Boundary Constrained-Generative Adversarial Network） was proposed based on user requirements and boundary constraints， and the proposed method consists of a constraint layout generator and a room relation discriminator. Firstly， the user-specified floorplan layout requirements （including the number and types of rooms and the adjacency relationship between houses） were transformed into a constraint relation graph structure， after that， the building boundary and constraint relation graph were encoded separately for feature fusion. Then， by adding the prediction module of bounding box， the constraint layout generator was used to convert the floorplan generation problem into a bounding box generation problem of each room object， and the geometric boundary optimization loss was used to solve the problems of severe occlusion between rooms and room getting out of the boundary. Finally， the room bounding box layout and the constraint relation graph were input into the room relation discriminator for training to generate the floorplan layout meeting the room objects and their relations. The Frechet Inception Distance （FID） and Structural Similarity Index Measure （SSIM） of the proposed method are improved by 4.39% and 2.3% compared with those of House-GAN on the large-scale real building dataset RPLAN. Experimental results show the proposed method improves the rationality and authenticity of the floorplan layout under different user requirements and boundary constraints.

Key words: floorplan generation, Graph Convolutional Network (GCN), Generative Adversarial Network (GAN), automatic layout, architectural design

摘要：

平面图设计是房屋设计的重要过程，而现有的自动化平面图设计方法缺乏考虑用户需求和建筑边界的共同约束，存在生成房间形状缺角、房间之间遮挡严重以及房间超越边界的布局不合理问题。针对上述问题，提出一种融合用户需求和边界约束的房屋平面图生成对抗网络（GBC-GAN），它由约束布局生成器和房间关系鉴别器构成。首先，将用户指定的房屋布局需求（包括房间数量和类型以及房屋之间的邻接方位关系）转化为约束关系图结构，之后对建筑边界和约束关系图分别编码并进行特征融合；然后，在约束布局生成器中引入边界框预测模块以将平面图生成问题转化为各房间对象边界框生成问题，并利用几何边界优化损失来解决房间之间遮挡严重、房间超越边界的问题；最后，将房间边界框布局和约束关系图输入到房间关系鉴别器训练生成符合房间对象及其关系的平面图布局。在大型真实建筑数据集RPLAN上，该方法的弗雷歇距离（FID）和结构相似性指数（SSIM）比House-GAN方法分别提升了4.39%和2.3%。实验结果表明，在不同用户需求和边界限制条件下，所提方法提高了房屋平面图的合理性和真实性。

关键词: 平面图生成, 图卷积神经网络, 生成对抗网络, 自动布局, 建筑设计