  In computer vision, the estimation of the fundamental matrix is a basic
problem that has been extensively studied. The accuracy of the estimation
imposes a significant influence on subsequent tasks such as the camera
trajectory determination and 3D reconstruction. In this paper we propose a new
method for fundamental matrix estimation that makes use of clustering a group
of 4D vectors. The key insight is the observation that among the 4D vectors
constructed from matching pairs of points obtained from the SIFT algorithm,
well-defined cluster points tend to be reliable inliers suitable for
fundamental matrix estimation. Based on this, we utilizes a recently proposed
efficient clustering method through density peaks seeking and propose a new
clustering assisted method. Experimental results show that the proposed
algorithm is faster and more accurate than currently commonly used methods.
