Vanishing Point Estimation in Uncalibrated Images with Prior Gravity
  Direction
Rémi Pautrat Shaohui Liu Petr Hruby Marc Pollefeys Daniel Barath
We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal
vanishing points, and the unknown focal length of the camera, leveraging a
prior vertical direction. The direction can come from an Inertial Measurement
Unit that is a standard component of recent consumer devices, e.g.,
smartphones. We provide an exhaustive analysis of minimal line configurations
and derive two new 2-line solvers, one of which does not suffer from
singularities affecting existing solvers. Additionally, we design a new
non-minimal method, running on an arbitrary number of lines, to boost the
performance in local optimization. Combining all solvers in a hybrid robust
estimator, our method achieves increased accuracy even with a rough prior.
Experiments on synthetic and real-world datasets demonstrate the superior
accuracy of our method compared to the state of the art, while having
comparable runtimes. We further demonstrate the applicability of our solvers
for relative rotation estimation. The code is available at
https://github.com/cvg/VP-Estimation-with-Prior-Gravity.