Research‎ > ‎

Street View Goes Indoors


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.


The videos below show sample navigation through the nine businesses discussed in the paper. The navigation maps were extracted by our algorithm.








  • Mohamed Aly and Jean-Yves Bouguet. Street View Goes Indoors: Automatic Pose Estimation From Uncalibrated Unordered Spherical Panoramas. 
    IEEE Workshop on Applications of Computer Vision (WACV), Colorado, January 2012. [pdf]


This is a joint work with Jean-Yves Bouguet.