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Linear perspectivecues deriving from regularities of the built environmentcan be used to recalibrate both intrinsic and extrinsic camera parametersonline, but these estimates can be unreliable due to irregularities in thescene, uncertainties in line segment estimation and background clutter. Here weaddress this challenge through four initiatives. First, we use the PanoContextpanoramic image dataset [27] to curate a novel and realistic dataset of planarprojections over a broad range of scenes, focal lengths and camera poses.Second, we use this novel dataset and the YorkUrbanDB [4] to systematicallyevaluate the linear perspective deviation measures frequently found in theliterature and show that the choice of deviation measure and likelihood modelhas a huge impact on reliability. Third, we use these findings to create anovel system for online camera calibration we call fR, and show that itoutperforms the prior state of the art, substantially reducing error inestimated camera rotation and focal length. Our fourth contribution is a noveland efficient approach to estimating uncertainty that can dramatically improveonline reliability for performance-critical applications by strategicallyselecting which frames to use for recalibration.

A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation