Clustering Assisted Fundamental Matrix Estimation
This addresses a basic problem in computer vision for tasks like camera trajectory determination and 3D reconstruction, but it is incremental as it builds on existing clustering techniques.
The paper tackles the problem of fundamental matrix estimation in computer vision by proposing a new method that uses clustering of 4D vectors from SIFT matches, resulting in faster and more accurate performance compared to existing methods.
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.