Omnidirectional image capture on mobile devices for fast automatic generation of 2.5D indoor maps
Giovanni Pintore, Valeria Garro, Fabio Ganovelli, Enrico Gobbetti, and Marco Agus
February 2016
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 omnidirectional 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.
Reference and download information
Giovanni Pintore, Valeria Garro, Fabio Ganovelli, Enrico Gobbetti, and Marco Agus. Omnidirectional image capture on mobile devices for fast automatic generation of 2.5D indoor maps. In Proc. IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 1-9, February 2016. DOI: 10.1109/WACV.2016.7477631.
Related multimedia productions
Bibtex citation record
@InProceedings{Pintore:2016:OIC, author = {Giovanni Pintore and Valeria Garro and Fabio Ganovelli and Enrico Gobbetti and Marco Agus}, title = {Omnidirectional image capture on mobile devices for fast automatic generation of {2.5D} indoor maps}, booktitle = {Proc. IEEE Winter Conference on Applications of Computer Vision (WACV)}, pages = {1--9}, month = {February}, year = {2016}, 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 omnidirectional 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. }, doi = {10.1109/WACV.2016.7477631}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintore:2016:OIC'}, }
The publications listed here are included as a means to ensure timely
dissemination of scholarly and technical work on a non-commercial basis.
Copyright and all rights therein are maintained by the authors or by
other copyright holders, notwithstanding that they have offered their works
here electronically. It is understood that all persons copying this
information will adhere to the terms and constraints invoked by each
author's copyright. These works may not be reposted without the
explicit permission of the copyright holder.
Please contact the authors if you are willing to republish this work in
a book, journal, on the Web or elsewhere. Thank you in advance.
All references in the main publication page are linked to a descriptive page
providing relevant bibliographic data and, possibly, a link to
the related document. Please refer to our main
publication repository page for a
page with direct links to documents.
+39 070 9250 1
+39 070 9250 216
(Secretary) katia
crs4.it
www.crs4.it/vic/
