We introduce the problem of scene viewpoint recognition, the goal of which is to classify the type of place shown in a photo, and also recognize the observer's viewpoint within that category of place. We construct a database of 360° panoramic images organized into 26 place categories. For each category, our algorithm automatically aligns the panoramas to build a full-view representation of the surrounding place. We also study the symmetry properties and canonical viewpoint of each place category. At test time, given a photo of a scene, the model can recognize the place category, produce a compass-like indication of the observer's most likely viewpoint within that place, and use this information to extrapolate beyond the available view, filling in the probable visual layout that would appear beyond the boundary of the photo.

@inproceedings{Xiao:2012:RSV,
   author = "Jianxiong Xiao and Krista A. Ehinger and Aude Oliva and Antonio Torralba",
   title = "Recognizing Scene Viewpoint using Panoramic Place Representation",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
   year = "2012",
   month = jun
}