@inproceedings{d5adeeb6eb9e4e1989524fbcbe0905cf,
title = "Omnidirectional image capture on mobile devices for fast automatic generation of 2.5D indoor maps",
abstract = "We introduce a light-weight automatic method to quickly capture and recover 2.5D multi-room indoor environments scaled to real-world metric dimensions. To minimize the user effort required, we capture and analyze a single omni-directional image per room using widely available mobile devices. Through a simple tracking of the user movements between rooms, we iterate the process to map and reconstruct entire floor plans. In order to infer 3D clues with a minimal processing and without relying on the presence of texture or detail, we define a specialized spatial transform based on catadioptric theory to highlight the room's structure in a virtual projection. From this information, we define a parametric model of each room to formalize our problem as a global optimization solved by Levenberg-Marquardt iterations. The effectiveness of the method is demonstrated on several challenging real-world multi-room indoor scenes.",
author = "Giovanni Pintore and Valeria Garro and Fabio Ganovelli and Enrico Gobbetti and Marco Agus",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; IEEE Winter Conference on Applications of Computer Vision, WACV 2016 ; Conference date: 07-03-2016 Through 10-03-2016",
year = "2016",
month = may,
day = "23",
doi = "10.1109/WACV.2016.7477631",
language = "English (US)",
series = "2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016",
address = "United States",
}