A semi-supervised approach for building wall layout segmentation based on transformers and limited data

Abstract

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%.