steps of eight state-of-the-art approaches. Sect. \[sec:exper\] describes the experimental setup including datasets, criteria and implementation details of the evaluated methods. Qualitative and quantitative experimental results are shown in Sect. \[sec:res\]. Summary and discussion are presented in Sect. \[sec:sum\]. Conclusions are finally drawn in Sect. \[sec:con\].

Related work {#sec:rela}
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This section briefly reviews the prior works of 2D correspondence selection including both parametric and non-parametric categories. Relevant evaluations in the field of feature matching are also discussed.

Correspondence selection methods
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For parametric methods, the most well-known algorithm is arguably RANSAC presented by Fischler et al. [@fischler1981random]. RANSAC iteratively explores the space of model parameters by randomly sampling and estimates the most reliable model based on the maximum number of inliers. Then, outliers can be removed using the generated model. Several variants of RANSAC such as MLESAC [@Torr2000MLESAC], LO-RANSAC [@Chum2003Locally], PROSAC [@Chum2005Matching] and USAC [@Raguram2013USAC] were proposed in the following decades. MLESAC employs the maximum likelihood estimation rather than the inlier count to check the solutions. LO-RANSAC inserts an optimization process where the generated model is refined by the subset of inliers. A weighted sampling step is adopted instead of random sampling in PROSAC. This method sorts the raw 