City-scale localization for cameras with known vertical direction
Journal article, 2017

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 percent outlier correspondences in city-scale models with several millions of 3D points.

position retrieval


camera pose


Linus Svärm

Lund University

Olof Enqvist

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Fredrik Kahl

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Magnus Oskarsson

Lund University

IEEE Transactions on Pattern Analysis and Machine Intelligence

0162-8828 (ISSN) 19393539 (eISSN)

Vol. 39 7 1455-1461 7534854

Subject Categories

Computer Vision and Robotics (Autonomous Systems)



More information

Latest update

4/5/2022 7