AI Image generators based on diffusion models are widely discussed recently
for their capability to create images from simple text prompts. But, for
practical use in civil engineering they need to be able to create specific
construction plans for given constraints. Within this paper we explore the
capabilities of those diffusion-based AI generators for computational design at
the example of floor plans and identify their current limitation. We explain
how the diffusion-models work and propose new diffusion models with improved
semantic encoding. In several experiments we show that we can improve validity
of generated floor plans from 6% to 90% and query performance for different
examples. We identify short comings and derive future research challenges of
those models and discuss the need to combine diffusion models with building
information modelling. With this we provide key insights into the current state
and future directions for diffusion models in civil engineering.

基于扩散模型的 AI 图像生成器近期广泛讨论，具备从简单文本提示中创建图像的能力。但是，在土木工程的实际应用中，它们需要能够根据给定的约束条件创建特定的施工计划。本文通过探索基于扩散的 AI 生成器在计算设计中的能力，以楼层平面图为例，辨识其目前的局限性。我们解释了扩散模型的工作原理，并提出了具备改进语义编码的新扩散模型。通过多个实验证明，我们可以将生成的楼层平面图的有效性从 6% 提高至 90%，并提高不同示例的查询性能。我们对这些模型的不足之处进行了辨识，并得出了未来的研究挑战，并讨论了将扩散模型与建筑信息模型相结合的需求。通过这些，我们提供了土木工程中扩散模型的当前状态和未来方向的关键见解。

计算设计中的扩散模型：以楼层平面图为例

Diffusion Models for Computational Design at the Example of Floor Plans

Digitalization of existing buildings and the creation of 3D BIM models for
them has become crucial for many tasks. Of particular importance are floor
plans, which contain information about building layouts and are vital for
processes such as construction, maintenance or refurbishing. However, this data
is not always available in digital form, especially for older buildings
constructed before CAD tools were widely available, or lacks semantic
information. The digitalization of such information usually requires manual
work of an expert that must reconstruct the layouts by hand, which is a
cumbersome and error-prone process. In this paper, we present a pipeline for
reconstruction of vectorized 3D models from scanned 2D plans, aiming at
increasing the efficiency of this process. The method presented achieves
state-of-the-art results in the public dataset CubiCasa5k, and shows good
generalization to different types of plans. Our vectorization approach is
particularly effective, outperforming previous methods.

本文介绍了一种从扫描的 2D 平面图重建矢量化 3D 模型的流程，以提高数字化过程的效率，该方法在公共数据集 CubiCasa5k 中实现了最先进的结果，并表现出较好的推广性，其矢量化方法特别有效，优于以前的方法。