Method for fundamental matrix estimation combined with feature lines

Abstract

Fundamental matrix estimation has been studied extensively in the area of computer vision and previously proposed techniques include those that only use feature points. In this study, we propose a new technique for calculating the fundamental matrix combined with feature lines, which is based on the epipolar geometry of horizontal and vertical feature lines. First, a method for parameterizing the fundamental matrix is introduced, where the camera orientation elements and relative orientation elements are used as the parameters of the fundamental matrix, and the equivalent relationships are deduced based on the horizontal and vertical feature lines. Next, the feature lines are used as the interior points by the RANSAC algorithm to search for the optimal feature point subset, before determining the weight of each factor using the M-estimators algorithm and building a unified adjustment model to estimate the fundamental matrix. The experimental results obtained using simulated images and real images demonstrate that the proposed approach is feasible in practice and it can greatly reduce the dependency on feature points in the traditional method, while the introduction of feature lines can improve the accuracy and stability of the results to some extent.