Abstract
We aim at estimating the fundamental matrix in two
views from five correspondences of rotation invariant features obtained by e.g. the SIFT detector. The proposed minimal solver1 first estimates a homography from three correspondences assuming that they are co-planar and exploiting
their rotational components. Then the fundamental matrix
is obtained from the homography and two additional point
pairs in general position. The proposed approach, combined with robust estimators like Graph-Cut RANSAC, is
superior to other state-of-the-art algorithms both in terms
of accuracy and number of iterations required. This is validated on synthesized data and 561 real image pairs. Moreover, the tests show that requiring three points on a plane
is not too restrictive in urban environment and locally optimized robust estimators lead to accurate estimates even
if the points are not entirely co-planar. As a potential application, we show that using the proposed method makes
two-view multi-motion estimation more accurate