accomplished using off-the-shelf detectors and descriptors, e.g., SIFT [@lowe2004distinctive]. To generate initial feature matches $\cal C$, keypoints are matched with each other based on feature similarity, i.e., a correspondence (match) in $\cal C$ is defined as $c=\{\mathbf{x},\mathbf{x}^{'},\arg \mathop {\max }\limits_{{\bf f}^{'}} \; {s_{\mathcal{F}(\mathbf{f},\mathbf{f}^{'})}} \}$ with $\mathbf{x}\in{\mathcal{K}}$, $\mathbf{x}^{'}\in{\mathcal{K}^{'}}$, $\mathbf{f}\in{\mathcal{F}}$, $\mathbf{f}^{'}\in{\mathcal{F}^{'}}$ and $s_{\mathcal{F}}$ being the feature similarity score. The objective of correspondence selection is digging out the maximum consensus (inlier) subset ${\mathcal{C}_{inlier}}\subseteq \mathcal{C}$. Core principles and computation steps of evaluated algorithms are given as follows.

**Nearest Neighbor Similarity Ratio [@lowe2004distinctive].** NNSR directly utilizes descriptor similarities to remove less distinctive matches. Specifically, the term equaling to the ratio of the closest to the second-closest feature distance to each correspondence is used as a penalty. Therefore, a correspondence is judged as inlier if $$\label{eq:LRF1}
{\frac{\parallel \mathbf{f}-{\mathbf{f}_{1}^{'}}{{\parallel }_{2}}}{\parallel \mathbf{f}-{\mathbf{f}_{2}^{'}}{{\parallel }_{2}}}\leq t_{nnsr}},$$ where $t_{nnsr}\in [0,1]$, ${{\| {\cdot} \|}_{2}}$ hereinafter denotes the $L_2$ norm (this distance metric is suggested in [@lowe2004distinctive]), $\mathbf{f}_{1}^{'}$ and $\mathbf{f}_{2}^{'}$ represent the most and the second most similar feature descriptors of $\mathbf{f}$, respectively. Values of threshold $t_{nnsr}$ and other mentioned thresholds in the following are presented in Table \[tab:para\].

**Random Sample Consensus [@fischler1981random].** RANSAC follows a hypothesize-and-verify framework by repeating procedures of random 