A Minimal Solution for Two-view Focal-length Estimation using Two Affine Correspondences
This addresses a previously unsolved problem in computer vision for applications like 3D reconstruction, though it is incremental as it extends existing point correspondence techniques.
The paper tackles the problem of estimating the common focal length and fundamental matrix between two semi-calibrated cameras using only two affine correspondences, presenting a minimal solution that is validated on synthetic data and 104 real image pairs with improved noise robustness.
A minimal solution using two affine correspondences is presented to estimate the common focal length and the fundamental matrix between two semi-calibrated cameras - known intrinsic parameters except a common focal length. To the best of our knowledge, this problem is unsolved. The proposed approach extends point correspondence-based techniques with linear constraints derived from local affine transformations. The obtained multivariate polynomial system is efficiently solved by the hidden-variable technique. Observing the geometry of local affinities, we introduce novel conditions eliminating invalid roots. To select the best one out of the remaining candidates, a root selection technique is proposed outperforming the recent ones especially in case of high-level noise. The proposed 2-point algorithm is validated on both synthetic data and 104 publicly available real image pairs. A Matlab implementation of the proposed solution is included in the paper.