a range of light directions. In 3D domain, Tombari et al. [@tombari2013performance] compared two categories (i.e., fixed-scale and adaptive-scale) of 3D feature detectors in terms of distinctiveness, repeatability and efficiency under the nuisances of viewpoint changes, clutter, occlusions and noise. Guo et al. [@guo2016comprehensive] tested the descriptiveness, robustness, compactness and efficiency of ten local geometric descriptors on eight datasets with radius variations, varying mesh resolution, Gaussian noise and etc. More relevant to our work is the evaluation performed by Raguram et al. [@Raguram2008A], where RANSAC and a set of its variants were examined under different ratios of inliers. This paper, compared with [@Raguram2008A], considers both parametric and non-parametric methods as well as a variety of nuisances for more comprehensive evaluation.

Considered methods {#sec:method}
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Eight 2D correspondence selection algorithms including two parametric ones, i.e., RANSAC [@fischler1981random] and USAC [@Raguram2013USAC], and six non-parametric ones, i.e., NNSR [@lowe2004distinctive], ST [@Leordeanu2005A], GTM [@albarelli2012imposing], VFC [@Ma2014Robust], GMS [@bian2017gms], LPM [@Ma2017Locality], are considered in our evaluation. Before introducing their theories, we give some general notations for better readability.

Given two images $(I,I^{'})$ to be matched, keypoints and local feature descriptors are computed for them as $(\mathcal{K}, \mathcal{K}^{'})$ and $(\mathcal{F}, \mathcal{F}^{'})$, respectively. This procedure can be 