验证了pix2pix可自主学习建筑空间拓扑关系，助力设计自动化。
Fluid Grey 2: How Well Does Generative Adversarial Network Learn Deeper Topology Structure in Architecture That Matches Images?
- 在pix2pix前后加入Grasshopper检测模块，实现拓扑关系自动识别。
- 实验显示灰度输入比RGB更利于拓扑学习，效率提升显著。
- 方法简单高效，适合建筑师参与设计与批量检测。
在建筑设计与城市更新中，空间内在与外在属性的区域特征至关重要，传统方法常依赖图像与图结构的GAN分步实现，但模型嵌套与数据转换易导致信息丢失。为此，本文提出一种快速检测pix2pix学习拓扑关系能力的方法，通过在GAN前后添加基于Grasshopper的两个检测模块，实现对学习过程的量化分析与可视化。研究发现，灰度输入相比RGB输入更有利于拓扑关系的学习，且该方法耗时短、操作简便。本文创新点在于：1）证明pix2pix具备自主学习空间拓扑关系的能力，并可应用于建筑设计；2）填补了从拓扑视角评估图像生成GAN性能的空白。所提方法适用于定制同拓扑结构的数据集及图像拓扑关系的批量检测，未来可为基于GAN的建筑设计与城市更新提供理论支持与数据依据。
原文摘要 · Abstract (English)
Taking into account the regional characteristics of intrinsic and extrinsic properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facilitate architects and users to participate in the design. Therefore, this study hopes to prove that I2I GAN also has the potential to recognize topological relationships autonomously. Therefore, this research proposes a method for quickly detecting the ability of pix2pix to learn topological relationships, which is achieved by adding two Grasshopper-based detection modules before and after GAN. At the same time, quantitative data is provided and its learning process is visualized, and changes in different input modes such as greyscale and RGB affect its learning efficiency. There are two innovations in this paper: 1) It proves that pix2pix can automatically learn spatial topological relationships and apply them to architectural design. 2) It fills the gap in detecting the performance of Image-based Generation GAN from a topological perspective. Moreover, the detection method proposed in this study takes a short time and is simple to operate. The two detection modules can be widely used for customizing image datasets with the same topological structure and for batch detection of topological relationships of images. In the future, this paper may provide a theoretical foundation and data support for the application of architectural design and urban renewal that use GAN to preserve spatial topological characteristics.
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