  We propose an approach for estimating the relative pose between calibrated
image pairs by jointly exploiting points, lines, and their coincidences in a
hybrid manner. We investigate all possible configurations where these data
modalities can be used together and review the minimal solvers available in the
literature. Our hybrid framework combines the advantages of all configurations,
enabling robust and accurate estimation in challenging environments. In
addition, we design a method for jointly estimating multiple vanishing point
correspondences in two images, and a bundle adjustment that considers all
relevant data modalities. Experiments on various indoor and outdoor datasets
show that our approach outperforms point-based methods, improving
AUC@10$^\circ$ by 1-7 points while running at comparable speeds. The source
code of the solvers and hybrid framework will be made public.
