Clustering Assisted Fundamental Matrix Estimation

TL;DR

This paper introduces a clustering-assisted approach for fundamental matrix estimation in computer vision, leveraging density peak clustering of 4D vectors from SIFT matches to improve accuracy and speed.

Contribution

It presents a novel clustering-based method that enhances fundamental matrix estimation by identifying reliable inliers through density peak clustering.

Findings

Faster fundamental matrix estimation compared to existing methods.

More accurate results in experiments.

Effective identification of inliers using clustering.

Abstract

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.