TY - GEN
T1 - Omnidirectional image capture on mobile devices for fast automatic generation of 2.5D indoor maps
AU - Pintore, Giovanni
AU - Garro, Valeria
AU - Ganovelli, Fabio
AU - Gobbetti, Enrico
AU - Agus, Marco
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/23
Y1 - 2016/5/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84977618629&partnerID=8YFLogxK
U2 - 10.1109/WACV.2016.7477631
DO - 10.1109/WACV.2016.7477631
M3 - Conference contribution
AN - SCOPUS:84977618629
T3 - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
BT - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Y2 - 7 March 2016 through 10 March 2016
ER -