In this paper, we present a GNN-based Line Segment Parser (GLSP), which uses a junction heatmap to predict line segments' endpoints, and graph neural networks to extract line segments and their categories. Different from previous floor plan recognition methods, which rely on semantic segmentation, our proposed method is able to output vectorized line segment and requires less post-processing steps to be put into practical use. Our experiments show that the methods outperform state-of-the-art line segment detection models on multi-class line segment detection tasks with floor plan images. In the paper, we use our floor plan dataset named Large-scale Residential Floor Plan data (LRFP). The dataset contains a total of 271,035 floor plan images. The label corresponding to each picture contains the scale information, the categories and outlines of rooms, and the endpoint positions of line segments such as doors, windows, and walls. Our augmentation method makes the dataset adaptable to the drawing styles of as many countries and regions as possible.

翻译：本文提出了一种基于图神经网络的线段解析器（GLSP），该模型利用交点热力图预测线段端点，并通过图神经网络提取线段及其类别。与依赖语义分割的传统平面图识别方法不同，本方法可直接输出矢量化线段，且仅需较少后处理步骤即可投入实际应用。实验表明，该方法在平面图像的多类别线段检测任务中优于现有最先进的线段检测模型。本文采用自主构建的"大规模住宅平面图数据集（LRFP）"，该数据集共包含271,035张平面图像。每张图像对应的标注信息包含：尺度参数、房间类别及轮廓、门窗墙等线段的端点坐标。本文提出的数据增强方案使数据集能够适应尽可能多的国家和地区绘图风格。