et al. [@Raguram2008A] tested the performance of a set of random sample consensus methods including the popular RANSAC and its variants. However, all these evaluations are either not in line with 2D correspondence selection or not comprehensive enough for an in-depth comparison. First, the critical step in correspondence selection is finding correspondence consensus, while feature detection and description aim at building high-quality initial feature correspondences (such quality is difficult to be guaranteed without correspondence selection [@lowe2004distinctive]). Second, the performance of non-parametric approaches and some recent algorithms remains unclear (only parametric methods were tested in [@Raguram2008A]).

In these regards, we present the first comprehensive evaluation, to the best of our knowledge, for 2D correspondence selection from different perspectives in a uniform experimental framework. The considered methods in our evaluation range from classical algorithms to the most recent ones, typically covering both parametric and non-parametric approaches. To be specific, RANSAC [@fischler1981random] and USAC [@Raguram2013USAC] are selected from the parametric family, as RANSAC is arguably the most popular parametric approach and USAC is a well-known modified version of RANSAC. As for non-parametric methods, we choose NNSR [@lowe2004distinctive] as a representative of those methods based on descriptor similarity constraints. ST [@Leordeanu2005A], GTM [@albarelli2012imposing] and 