NadirFloorNet: reconstructing multi-room floorplans from a small set of registered panoramic images
Giovanni Pintore, Uzair Shah, Marco Agus, and Enrico Gobbetti
June 2025
Abstract
We introduce a novel deep-learning approach for predicting complex indoor floor plans with ceiling heights from a minimal set of registered 360-degree images of cluttered rooms. Leveraging the broad contextual information available in a single panoramic image and the availability of annotated training datasets of room layouts, a transformer-based neural network predicts a geometric representation of each room’s architectural structure, excluding furniture and objects, and projects it on a horizontal plane (the Nadir plane) to estimate the disoccluded floor area and the ceiling heights. We then merge and process these Nadir representations on the same floor plan, using a deformable attention transformer that exploits mutual information to resolve structural occlusions and complete room reconstruction. This fully data- driven solution achieves state-of-the-art results on synthetic and real-world datasets with a minimal number of input images.
Reference and download information
Giovanni Pintore, Uzair Shah, Marco Agus, and Enrico Gobbetti. NadirFloorNet: reconstructing multi-room floorplans from a small set of registered panoramic images. In 2nd CVPR Workshop on Urban Scene Modeling. Pages 1986-1994, June 2025. IEEE.
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Bibtex citation record
@inproceedings{Pintore:2025:NRM, author = {Giovanni Pintore and Uzair Shah and Marco Agus and Enrico Gobbetti}, title = {{NadirFloorNet}: reconstructing multi-room floorplans from a small set of registered panoramic images}, booktitle = {2nd CVPR Workshop on Urban Scene Modeling}, pages = {1986-1994}, publisher = {IEEE}, month = {June}, year = {2025}, abstract = { We introduce a novel deep-learning approach for predicting complex indoor floor plans with ceiling heights from a minimal set of registered 360-degree images of cluttered rooms. Leveraging the broad contextual information available in a single panoramic image and the availability of annotated training datasets of room layouts, a transformer-based neural network predicts a geometric representation of each room’s architectural structure, excluding furniture and objects, and projects it on a horizontal plane (the Nadir plane) to estimate the disoccluded floor area and the ceiling heights. We then merge and process these Nadir representations on the same floor plan, using a deformable attention transformer that exploits mutual information to resolve structural occlusions and complete room reconstruction. This fully data- driven solution achieves state-of-the-art results on synthetic and real-world datasets with a minimal number of input images. }, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintore:2025:NRM'}, }
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