# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 3 Experimental Results and Discussion
### Results
#### 3.5.1 Multi-View
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \begin{tabular}{c} **Detector** \\ **Descriptor** \\ \end{tabular} & **SIFT + SIFT** & **SURF + SURF** & **SURF + SIFT** & **MSD + SIFT** & **ASIFT** & **R2D2** & **D2-NET** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Visible-Thermal** \\ **(Figure 6a)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 0 & 1 & 1 & 4 & 3 & 1 & **53** \\  & & (0\%) & (3,2\%) & (4,2\%) & (22,2\%) & (2,9\%) & (6,25\%) & **(81,54\%)** \\  & & 273.7 & 143.8 & 172.2 & 63.1 & 202.7 & 184.3 & **13.7** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **SEM** \\ **(Figure 6b)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 0 & 0 & 3 & 0 & 6 & 5 & **16** \\  & & (0\%) & (0\%) & (33.3\%) & (0\%) & (28.6\%) & (62.5\%) & **(76.2\%)** \\  & & 1470.0 & 983.4 & 22.0 & 860.0 & 1013.4 & 10.7 & **6.9** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Visible-Range** \\ **(Figure 6c)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 1 & 13 & 154 & 3 & 47 & 5 & **77** \\  & & (3,3\%) & (25\%) & (53,1\%) & (23,1\%) & (32,9\%) & (31,3\%) & **(77,8\%)** \\  & & 216.9 & 141.9 & **34.4** & 136.1 & 140.8 & 88.2 & 43.4 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Visible-Thermal** \\ **Aerial** \\ **(Figure 6d)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 0 & 3 & 9 & 0 & 26 & 17 & **107** \\  & & (0\%) & (30\%) & (60\%) & (0\%) & (86.7\%) & **(100\%)** & (97.3\%) \\  & & 383.9 & 159.2 & 15.9 & 259.3 & 30.7 & **3.9** & 4.3 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Satellite** \\ **(Figure 6e)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 0 & 0 & 7 & 9 & 0 & 6 & **135** \\  & & (0\%) & (0\%) & (41,17\%) & (75\%) & (0\%) & (75\%) & **(95,7)** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Magnetic** \\ **Resonance** \\ **(Figure 6f)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 0 & 0 & 10 & 0 & 0 & 5 & **44** \\  & & (0\%) & (0\%) & (66.7\%) & (0\%) & (0\%) & (16.4\%) & **(92.6\%)** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Magnetic** \\ **Resonance** \\ **(Figure 6f)** \\ \end{tabular} } & \multirow{2}{*}{**CM**} & 0 & 0 & 10 & 0 & 0 & 5 & **44** \\  & & (0\%) & (0\%) & (66.7\%) & (0\%) & (0\%) & (16.4\%) & **(92.6\%)** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Magnetic** \\ **Resonance** \\ **(Figure 6f)** \\ \end{tabular} } & \multirow{2}{*}{**ME** ** (px)**} & 194.0 & 151.9 & 8.2 & 122.9 & 144.1 & 30.2 & **5.3** \\ \hline \end{tabular}
\end{table} 
Table 2: Number of correct matches (CM) with percentage and mean error (ME) (in px) for the different hand-crafted and learning-based algorithms in the seven multi-modal datasets. The best results are highlighted in bold. outperforms the accuracy of other algorithms for the facade and forest dataset, which are characterized by repetitive features (i.e., windows and canopy). However, ASIFT, which is specifically designed to deal with large perspective distortions, was the only algorithm capable of registering the images in the graffiti dataset with high geometric differences (Figure 5f). This is probably due to the lack of training of the chosen matching learning algorithms for this particular case. It is also worth noting that ASIFT is a technique that simulates different affine distortions to the images, and a similar technique can work with different detectors and descriptors, so the combination of ASFIT and learning-based algorithms would be possible. In order to evaluate this tool in comparison to other commercial and open-source software, a 3D reconstruction for each multi-view dataset was carried out using Agisoft Metashape 2.0.1 and GRAPHOS [1]. Acceptable results were obtained only for the fifth multi-view dataset (Graffiti with low differences, Figure 5e), while both software failed to compute a 3D reconstruction for the rest of the datasets.