Generalizing Floor Plans Using Graph Neural Networks

The proliferation of indoor maps is limited by the manual process of generalizing floor plans. Previous attempts at automating similar processes use rasterization for structure. With Graph Neural Networks (GNN) it is now possible to skip rasterization and rely on the inherent structures in CAD drawings. A core component in floor plan generalization is localization of doors. We show how floor plan graphs can be extracted directly from CAD primitives and how state-of the-art GNNs can be used to classify graph nodes as door or non-door. Generalization is represented by the creation of placeholder bounding boxes using the labelled graph nodes. Our graph-based approach completely outperforms the Faster R-CNN baseline, which fail to locate any doors with the desired localization accuracy. To support further development of graph-based methods and comparison with raster-based methods, we publish a new dataset that consists of both image and graph-based floor plan representations. Code and dataset is available at https://github.com/Chrps/MapGeneralization.