In structural design, accurately extracting information from floor plan drawings of buildings is essential for building 3D models and facilitating design automation. However, deep learning models often face challenges due to their dependence on large labeled datasets, which are labor and time‐intensive to generate. And floor plan drawings often present challenges, such as overlapping elements and similar geometric shapes. This study introduces a semi‐supervised wall segmentation approach (SWS), specifically designed to perform effectively with limited labeled data. SWS combines a deep semantic feature extraction framework with a hierarchical vision transformer and multi‐scale feature aggregation to refine feature maps and maintain the spatial precision necessary for pixel‐wise segmentation. SWS incorporates consistency regularization to encourage consistent predictions across weak and strong augmentations of the same image. The proposed method improves an intersection over union by more than 4%.

中文翻译：

---

一种基于变压器和有限数据的建筑墙体布局分割的半监督方法


在结构设计中，从建筑物的平面图中准确提取信息对于构建 3D 模型和促进设计自动化至关重要。然而，深度学习模型由于依赖于大型标记数据集，而生成这些数据集需要耗费人力和时间。平面图通常会带来挑战，例如重叠的元素和相似的几何形状。本研究介绍了一种半监督壁分割方法 （SWS），专门用于对有限的标记数据进行有效执行。SWS 将深度语义特征提取框架与分层视觉转换器和多尺度特征聚合相结合，以优化特征图并保持像素分割所需的空间精度。SWS 结合了一致性正则化，以鼓励对同一图像的弱增强和强增强进行一致的预测。所提出的方法将交集优于并集的改进提高了 4% 以上。