similarity constraint or geometric constraint, such as the nearest neighbor similarity ratio (NNSR) [@lowe2004distinctive], spectral technique (ST) [@Leordeanu2005A], game-theoretic matching (GTM) [@albarelli2012imposing], graph-based affine invariant matching (GAIM) [@Collins2014An] and locality preserving matching (LPM) [@Ma2017Locality]. There are also constraint-independent non-parametric methods such as identifying point correspondences by correspondence function (ICF) [@Li2010Rejecting], vector field consensus (VFC) [@Ma2014Robust], grid-based motion statistics (GMS) [@bian2017gms] and coherence based decision boundaries (CODE) [@Lin2017CODE]. With the wealth of existing correspondence selection methods, however, it is on the one hand difficult for developers to choose the most proper method given a specific application and on the other hand confusing for researchers to compare these methods under different conditions. This problem is mainly due to the fact that most methods were tested under a specific application scenario and compared with a limited number of baselines.

Some performance evaluations in the field of image feature matching also exist. For instance, Mikolajczyk et al. [@mikolajczyk2005performance] and Heinly et al. [@Heinly2012Comparative] evaluated the performance of several 2D feature descriptors. Aans et al. [@Aan2012Interesting] investigated the performance of 2D feature detectors. Moreels et al. [@moreels2007evaluation] performed an aggregated evaluation of both 2D detectors and descriptors. In addition to feature detectors and descriptors, Raguram 