Street view goes indoors: Automatic pose estimation from uncalibrated unordered spherical panoramas

Mohamed Aly*, Jean Yves Bouguet

*Corresponding author for this work

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

16 Scopus citations

Abstract

We present a novel algorithm that takes as input an uncalibrated unordered set of spherical panoramic images and outputs their relative pose up to a global scale. The panoramas contain both indoor and outdoor shots and each set was taken in a particular indoor location e.g. a bakery or a restaurant. The estimated pose is used to build a map of the location, and allow easy visual navigation and exploration in the spirit of Google's Street View. We also present a dataset of 9 sets of panoramas, together with an annotation tool and ground truth point correspondences. The manual annotations were used to obtain ground truth relative pose, and to quantitatively evaluate the different parameters of our algorithm, and can be used to benchmark different approaches. We show excellent results on the dataset and point out future work.

Original languageEnglish (US)
Title of host publication2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
Pages1-8
Number of pages8
DOIs
StatePublished - May 11 2012
Event2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012 - Breckenridge, CO, United States
Duration: Jan 9 2012Jan 11 2012

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986

Other

Other2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
CountryUnited States
CityBreckenridge, CO
Period01/9/1201/11/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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