motion statistics (GMS) [@bian2017gms]. VFC supposes that the noise around inliers and outliers falls in different distributions. This approach estimates the probability of inliers by the maximum likelihood estimation for parameters in the mixture probabilistic model. Additionally, GMS rejects false matches by counting the quantity of matches in small neighborhoods and achieves real-time performance with an efficient grid-based score estimator.

Other evaluations
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In the feature matching field, some evaluations of 2D/3D local descriptors and detectors have been performed. For instance, Mikolajczyk et al. [@mikolajczyk2005performance] evaluated the performance of 2D feature descriptors under transformations of rotation, zoom, viewpoint change, blur, JPEG compression, light change and keypoint localization errors. Moreels et al. [@moreels2007evaluation] conducted an evaluation of several groups of 2D feature detectors and descriptors on images captured from the same 3D object with different viewpoints and lighting conditions. Heinly et al. [@Heinly2012Comparative] performed an evaluation of several 2D binary descriptors, aiming at testing their descriptiveness under different feature detectors on several scenes with illumination change, viewpoint change, pure camera rotation and pure scale change. Aans et al. [@Aan2012Interesting] investigated the performance of several 2D feature detectors on a particular dataset wherein each scene was depicted from 119 camera positions with 