# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 4 Results
### MapMatcher  
Then, we apply the MapReduce proposed in this study to historical map data and compare its performance with several popular models in recent years, including RIFT2 [7], D2 Net [23], SuperGlue [12], DeDoDe [24], R2D2 [22], and RoMa [11]. We show the results in Table 3 using the a-h image in Figure 6.  
Runtime (RT) analysis demonstrates that MapReduce and R2D2 achieve substantially lower latency compared to baseline methods. Baselines including RIFT2, D2 Net, SuperGlue, and DeDoDe exhibit inference times ranging from 1 to over 4 s. D2 Net reaches peak latencies of 4-5 s (e.g., 4.651 s on task a-c), while SuperGlue operates within 1-2 s. R2D2 maintains sub-2-second latency, with minima reaching 0.56s (task a-c), demonstrating computational efficiency. MapReduce achieves near-real-time performance (such as 1.063 s on a-e and 1.062 s on a-g), securing its position as a top-tier efficient method. According to the number of extracted keypoints (Ref and Sen), RIFT2, SuperGlue and RoMa tend to extract a very large number of keypoints (Ref and Sen often exceed 4000-6000), which can be computationally heavy and might not always be necessary. D2 Net and DeDoDe extract fewer keypoints (around a few hundred to a little over a thousand), resulting in less data for matching. R2D2 balances between extremes but still produces fewer keypoints (around 1000-2000). MapReduce strikes a moderate balance: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **Metrics** & **a–b** & **a–c** & **a–d** & **a–e** & **a–f** & **a–g** & **a–h** \\ \hline \multirow{4}{*}{RIFT2} & RT & 2.067 s & 2.730 s & 2.321 s & 2.032 s & 3.145 s & 2.659 s & 2.675 s \\  & Ref & **6099** & **6099** & **6099** & **6099** & **6099** & **6099** & **6099** \\  & Sen & 6373 & **6659** & 4999 & 4792 & 5855 & 4765 & **6127** \\  & \(N_{all}\) & 2059 & 986 & **1090** & 1451 & **2237** & 1530 & 1439 \\ \hline \multirow{4}{*}{D2 Net} & RT & 4.320 s & 4.651 s & 4.376 s & 5.011 s & 5.234 s & 4.012 s & 4.368 s \\  & Ref & 4316 & 4316 & 4316 & 4316 & 4316 & 4316 & 4316 \\  & Sen & 3975 & 4399 & 4361 & 3667 & 3872 & 4785 & 4800 \\  & \(N_{all}\) & 293 & 144 & 161 & 314 & 330 & 138 & 499 \\ \hline \multirow{4}{*}{SuperGlue} & RT & 1.786 s & 1.329 s & 1.345 s & 1.377 s & 2.153 s & 2.356 s & **1.038 s** \\  & Ref & 6223 & 6223 & 6223 & 6223 & 6223 & 6223 & 6223 & 6223 \\  & Sen & 5620 & 5727 & **6971** & **6615** & 5047 & **5713** & 5759 \\  & \(N_{all}\) & 241 & **1796** & 133 & **1762** & 523 & 375 & 357 \\ \hline \multirow{4}{*}{DeDoDe} & RT & 2.435 s & 3.025 s & 3.152 s & 2.125 s & 2.158 s & 2.568 s & 2.325 s \\  & Ref & 1620 & 1620 & 1620 & 1620 & 1620 & 1620 & 1620 & 1620 \\  & Sen & 1271 & 2320 & 1197 & 1629 & 1950 & 2739 & 2847 \\  & \(N_{all}\) & 57 & 208 & 76 & 285 & 79 & 231 & 274 \\ \hline \multirow{4}{*}{R2D2} & RT & **0.894 s** & **0.561 s** & 1.357 s & 1.639 s & **0.864 s** & 1.173 s & 1.269 s \\  & Ref & 1518 & 1518 & 1518 & 1518 & 1518 & 1518 & 1518 & 1518 \\  & Sen & 1445 & 2658 & 1871 & 1916 & 2779 & 1141 & 2159 \\  & \(N_{all}\) & 91 & 169 & 50 & 114 & 157 & 138 & 145 \\ \hline \multirow{4}{*}{RoMa} & RT & 1.939 s & 1.704 s & 1.548 s & 2.492 s & 1.875 s & 1.640 s & 1.801 s \\  & Ref & 5796 & 5796 & 5796 & 5796 & 5796 & 5796 & 5796 \\  & Sen & **6847** & 5369 & 5083 & 5341 & **6422** & 5967 & 5718 \\  & \(N_{all}\) & 1817 & 1394 & 1023 & 1115 & 1317 & 531 & 641 \\ \hline \multirow{4}{*}{**MapMatcher**} & RT & 1.079 s & 1.094 s & **1.094 s** & **1.063 s** & 1.092 s & **1.062 s** & 1.067 s \\  & Ref & 3559 & 3559 & 3559 & 3559 & 3559 & 3559 & 3559 \\ \cline{1-1}  & Sen & 2425 & 4144 & 3876 & 2959 & 4034 & 2893 & 3136 \\ \cline{1-1}  & \(N_{all}\) & **2418** & 501 & 303 & 630 & 252 & **2400** & **1829** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experiment results of different feature matching models. approximately 3500 reference and around 2400 sensed keypoints, which is sufficient to ensure robust matching without excessive overhead.