Room Classification on Floor Plan Graphs using Graph Neural Networks
This work addresses room classification for architectural or real estate applications, but it is incremental as it applies existing graph neural network methods to a specific dataset.
The paper tackled the problem of room classification on floor plan maps by representing them as graphs and using graph neural networks, achieving accuracies of 80% and 81% with GraphSAGE and Topology Adaptive GCN, outperforming a baseline multilayer perceptron by over 15%.
We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories. Rooms in the floor plans are represented as nodes in the graph with edges representing their adjacency in the map. We experiment with House-GAN dataset that consists of floor plan maps in vector format and train multilayer perceptron and graph neural networks. Our results show that graph neural networks, specifically GraphSAGE and Topology Adaptive GCN were able to achieve accuracy of 80% and 81% respectively outperforming baseline multilayer perceptron by more than 15% margin.