Abstract
Abstract. Fundamental Matrix Estimation is of vital importance in many vision applications and is a core part of 3D reconstruction pipeline. Radial distortion makes the problem to be numerically challenging. We propose a novel robust method for radial fundamental matrix estimation. Firstly, two-sided radial fundamental matrix is deduced to describe epipolar geometry relationship between two distorted images. Secondly, we use singular value decomposition to solve the final nonlinear minimization solutions and to get the outliers removed by multiplying a weighted matrix to the coefficient matrix. In every iterative step, the criterion which is the distance between feature point and corresponding epipolar line is used to determine the inliers and the weighted matrix is update according to it. The iterative process has a fast convergence rate, and the estimation result of radial fundamental matrix remains stable even at the condition of many outliers. Experimental results prove that the proposed method is of high accuracy and robust for estimating the radial fundamental matrix. The estimation result of radial fundamental matrix could be served as the initialization for structure from motion.
Highlights
Fundamental matrix describes the epipolar geometry relationship between two images in the same scene
We propose a new robust method for estimating the fundamental matrix with radial distortion based on 14 image correspondences
The mean and standard deviation of distance from point to epipolar line is listed in Table 1, from which we can see that under the condition of high level of outliers and false matches in the test dataset, the proposed method can eliminate the potential outliers to a certain extent, reducing its influence on the estimation results of the radial fundamental matrix
Summary
Fundamental matrix describes the epipolar geometry relationship between two images in the same scene. The five point relative pose solver with known camera internal parameters (Stewénius H, 2006) and the six point relative pose solver with unknown focal length (Stewenius H, 2005), the well-known 7-point and normalized 8-point algorithm (ArmanguéX, 2003) were the frequently used linear estimation algorithm These methods had high computational efficiency regardless of false matches and outliers, but poor accuracy and stability if taking these factors into account. A new formulation in which distortion center can be absorbed into the radial fundamental matrix was presented by (Brito J, 2013) These solutions make great progress in numerical stability and efficiency. We propose a new robust method for estimating the fundamental matrix with radial distortion based on 14 image correspondences.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
