  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.
