Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction

TL;DR

This paper introduces new methods for estimating vanishing points and camera focal length in uncalibrated images using prior gravity direction, improving accuracy and robustness over existing techniques.

Contribution

It presents two novel 2-line solvers that avoid singularities and a non-minimal method for enhanced local optimization, combined in a hybrid estimator.

Findings

Achieves higher accuracy than state-of-the-art methods

Maintains comparable runtime performance

Effective in synthetic and real-world datasets

Abstract

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.