Omnidirectional image capture on mobile devices for fast automatic generation of 2.5D indoor maps

Giovanni Pintore, Valeria Garro, Fabio Ganovelli, Enrico Gobbetti, Marco Agus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period03/7/1603/10/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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