& OPENCV & $t_{nnsr}$ & Adaptive [@otsu1979threshold]\
2 & RANSAC [@fischler1981random] & OPENCV & $t_{ransac}$ & 10*pix*\
& & & $n_{ransac}$ & 2000\
3 & ST [@Leordeanu2005A] & MATLAB & $t_{st}$ & 0.3\
4 & GTM [@albarelli2012imposing] & OPENCV & $t_{gtm}$ & Adaptive [@otsu1979threshold]\
& & & $n_{gtm}$ & 100\
& & & $\lambda{gtm}$ & 0.0001\
5 & USAC [@Raguram2013USAC] & OPENCV & $n_{usac}$ & 850000\
& & & $t_{\mathbf{H}}$ & 10*pix*\
& & & $t_{\mathbf{F}}$ & 1.5*pix*\
6 & VFC [@Ma2014Robust] & OPENCV & $\beta $ & 0.1\
& & & $\lambda_{vfc} $ & 3\
& & & $t_{vfc} $ & 0.75\
& & & $\gamma $ & 0.9\
7 & GMS [@bian2017gms] & OPENCV & $\alpha $ & 4\
8 & LPM [@Ma2017Locality] & MATLAB & $\lambda_{lpm}$ & 6\
& & & $k$ & 4\

Notably, for NNSR and GTM we set $t_{nnsr}$ and $t_{gtm}$ adaptively using the OTSU [@otsu1979threshold] algorithm to reduce thresholding errors as proper thresholds may vary in different scenarios even in different images.

All those methods are implemented in OPENCV or MATLAB with a PC equipped with a 3.2GHz processor and 8GB memory.

Datasets
--------

We perform our experiments on four datasets, i.e., VGG 