Correspondence estimation in image pairs

Abstract

This article provides an overview of current techniques for dense geometric correspondence estimation. We will first formally define geometric correspondence and investigate the different types of image pairs. Then we briefly look at the classic approaches to correspondence estimation, at their feasibility and flaws for simultaneous dense estimation. We will focus on the Bayesian approach, which is suited very well for this task and for which several promising algorithms have recently been developed. After having a look at the future of the Bayesian approaches, we conclude with a discussion