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On Sampling Focal Length Values to Solve the Absolute Pose Problem
Torsten Sattler1, Chris Sweeney2, and Marc Pollefeys1
1Department of Computer Science, ETH Zürich, Zürich, Switzerland
2University of California Santa Barbara, Santa Barbara, USA
Abstract. Estimating the absolute pose of a camera relative to a 3D representation of a scene is a fundamental step in many geometric Computer Vision applications. When the camera is calibrated, the pose can be computed very efficiently. If the calibration is unknown, the problem becomes much harder, resulting in slower solvers or solvers requiring more samples and thus significantly longer run-times for RANSAC. In this paper, we challenge the notion that using minimal solvers is always optimal and propose to compute the pose for a camera with unknown focal length by randomly sampling a focal length value and using an efficient pose solver for the now calibrated camera. Our main contribution is a novel sampling scheme that enables us to guide the sampling process towards promising focal length values and avoids considering all possible values once a good pose is found. The resulting RANSAC variant is significantly faster than current state-of-the-art pose solvers, especially for low inlier ratios, while achieving a similar or better pose accuracy.
Keywords: RANSAC, n-point-pose (P
LNCS 8692, p. 828 ff.
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© Springer International Publishing Switzerland 2014