2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IP-2.10
Paper Title GENERALIZING FLOOR PLANS USING GRAPH NEURAL NETWORKS
Authors Christoffer Plovmand Simonsen, Frederik Myrup Thiesson, Mark Philip Philipsen, Thomas Baltzer Moeslund, Aalborg University, Denmark
SessionMLR-APPL-IP-2: Machine learning for image processing 2
LocationArea E
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.