# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 3 Experimental Results and Discussion
### Assessment  
PhotoMatch provides a reference data editor (Figure 3) that allows for the end-user to select the reference keypoints with subpixel accuracy and compute the error for each point using the homography or fundamental matrix adjustment. Using this reference data editor, each imagery dataset was registered using a set of at least 12 manually selected keypoints and their corresponding matchings. The maximum error for these points was below one pixel for all image pairs.  
Once the reference keypoints and matchings are defined, PhotoMatch computes the homography or fundamental matrix transformation between each pair of images. After the keypoints are extracted by each algorithm (detector and descriptor), their coordinates are evaluated through comparison with the reference coordinates obtained via homography, or by computing the distance between each point and the line determined by the collinearity condition in the case of a fundamental matrix transformation (Tables 1 and 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Detector** & **SIFT +** & **SURF + SURF** & **SURF + SIFT** & **MSD +** & **ASIFT** & **R2D2** & **D2-NET** \\  & **Descriptor** & **SIFT** & **SIFT** & **SIFT** & **SIFT** & **SIFT** & **SIFT** & **SIFT** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Facade** \\ **(Figure 5a)** \\ \end{tabular} } & **CM** & 24 (27.2\%) & 26 (22.7\%) & 47 (33.7\%) & 19 (34.8\%) & **190** & 68 **(52.5\%)** & 27 (32.7\%) \\  & **ME (px)** & 175.1 & 170.6 & 163.8 & 123.2 & 172.2 & **110.5** & 176.9 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Forest** \\ **(Figure 5b)** \\ \end{tabular} } & **CM** & 108 (79.4\%) & 89 (71.2\%) & 139 (80.7\%) & 77 (80.6\%) & **1155** & 187 (86.4\%) & **1155** & 187 (**94.1\%)** \\  & **ME (px)** & 19.2 & 21.1 & 8.9 & 8.7 & 13.5 & **4.1** & 6.3 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Playground** \\ **(Figure 5c)** \\ \end{tabular} } & **CM** & 8 (36.9\%) & 51 (57.2\%) & 60 (60.7\%) & 30 (65.6\%) & **214** & 47 (**80.5\%)** & 49 (69.2\%) \\  & **ME (px)** & 607.8 & 224.6 & 66.5 & 40.6 & 137.5 & **26.3** & 45.9 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Boulder** \\ **(Figure 5d)** \\ \end{tabular} } & **CM** & 150 (80.1\%) & 261 (83.2\%) & 283 (86.4\%) & 24 (77.8\%) & **551** & 322 (96.1\%) & 533 (91.4\%) \\  & **ME (px)** & 50.9 & 49.9 & 30.3 & 163.5 & **12.6** & 29.8 & 20.3 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Graffit low** \\ **differences** \\ **(Figure 5e)** \\ \end{tabular} } & **CM** & 681 (99.9\%) & 613 (99.4\%) & 602 (96.8\%) & 62 (93.3\%) & **8713** & **8713** & **899.9\%)** & 241 (98.4\%) & 165 (94.9\%) \\  & **ME (px)** & **1.2** & 2.4 & 3.4 & 14.4 & 1.3 & 2.9 & 8.0 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Graffit high** \\ **differences** \\ **(Figure 5b)** \\ \end{tabular} } & **CM** & 91 (94.4\%) & 62 (90.9\%) & 50,5 (87.1\%) & 1 (10.3\%) & **2182** & 2 (38.7\%) & 2 (30\%) \\  & **ME (px)** & 17.0 & 18.6 & 31.7 & 241.0 & **1.4** & 151.3 & 171.0 \\ \hline \end{tabular}
\end{table}
Table 1: Number of correct matches (CM) with percentage and mean error (ME) for the different hand-crafted and learning-based algorithms and the six multi-view datasets. The best results are highlighted in bold.