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Title: City-Scale Localization for Cameras with Known Vertical Direction
Full text: PDF
Authors: Svärm, Linus and Enqvist, Olof and Kahl, Fredrik and Oskarsson, Magnus
Year: 2016
Publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Type:Journal Paper
Status: In Press
Refereed: Yes
BibTeX item:BibTeX
Abstract: We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle this problem, we use recent theoretical as well as technical advances. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite cases with more than 99% outlier correspondences in city-scale models with several millions of 3D points.

 

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