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 solver 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.

使用SIFT检测器获取的五个旋转不变特征对，估计两视图中的基础矩阵，首先从三个对应点估计一个单应性矩阵，然后从该矩阵和另外两个位置一般的点对中获取基本矩阵，结合Graph-Cut RANSAC等鲁棒估计器优于其他最先进的算法，在准确性和所需迭代次数方面得到验证。作为潜在应用，我们展示了使用所提出的方法使得两视图多运动估计更加准确。

未标定相机的五点基础矩阵估计