# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 5 Experimental Evaluation
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**DLALoc with 100 Anchors and 50 RANSAC Sampling Times**} \\ \hline  & \multicolumn{2}{c}{**Aachen**} & \multicolumn{2}{c}{**RobotCar**} \\  & \multicolumn{2}{c}{**Day**} & \multicolumn{2}{c}{**Night**} & \multicolumn{2}{c}{**Dusk**} & \multicolumn{2}{c}{**Sun**} & \multicolumn{2}{c}{**Night**} \\ \cline{2-5}
**Distance [m]** & 0.25/0.50/5.0 & 0.5/1.0/5.0 & 0.25/0.50/5.0 & 0.25/0.50/5.0 & 0.25/0.50/5.0 \\
**Orient. [deg]** & 2/5/10 & 2/5/10 & 2/5/10 & 2/5/10 & 2/5/10 \\ \hline NV+SIFT+NN & 84.9/88.4/94.3 & 45.4/58.1/76.2 & 58.2/86.3/95.1 & 50.1/71.2/93.1 & 7.8/13.6/26.1 \\ NV+SIFT+SG & 84.9/89.1/95.6 & 57.6/74.8/89.9 & 63.4/88.9/96.2 & 64.1/75.6/94.8 & 8.5/20.3/37.9 \\ NV+SP+NN & 84.7/89.6/93.9 & 42.6/58.9/76.9 & 60.1/87.1/96.4 & 59.8/72.8/93.9 & 7.5/19.6/34.2 \\ NV+SP+SG & 78.9/95.4/97.9 & 74.6/89.6/49.8 & 68.4/88.9/96.9 & 57.2/69.9/94.5 & 7.9/19.6/32.4 \\ HF-Net+NN & 74.6/90.9/94.5 & 55.6/68.9/93.8 & 58.4/89.5/96.4 & 49.3/77.9/96.9 & 4.5/8.6/20.4 \\ HF-Net+SG & 88.6/94.4/95.6 & 66.1/87.2/96.6 & 57.8/89.4/96.7 & 47.1/68.9/92.4 & 5.0/19.6/27.5 \\ \hline \end{tabular}
\end{table} 
Table 4: Localization results of DLALoc with 100 anchors and 50 RANSAC sampling times. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{5}{c}{**DLALoc with 100 Anchors and 25 RANSAC Sampling Times**} \\ \hline  & \multicolumn{2}{c}{**Aachen**} & \multicolumn{2}{c}{**RobotCar**} \\  & \multicolumn{2}{c}{**Day**} & \multicolumn{2}{c}{**Night**} & \multicolumn{2}{c}{**Dusk**} & \multicolumn{2}{c}{**Sun**} & \multicolumn{2}{c}{**Night**} \\
**Distance [m]** & 0.25/0.50/5.0 & 0.5/1.0/5.0 & 0.25/0.50/5.0 & 0.25/0.50/5.0 & 0.25/0.50/5.0 \\
**Orient. [deg]** & 2/5/10 & 2/5/10 & 2/5/10 & 2/5/10 & 2/5/10 \\ \hline NV+SIFT+NN & 81.9/87.1/**93.2** & 34.4/47.1/**63.1** & 56.3/84.5/94.9 & 45.9/68.1/**92.4** & 5.0/10.6/**24.7** \\ NV+SIFT+SG & 83.8/87.6/**94.5** & 54.3/72.5/**88.1** & 61.2/86.2/95.3 & 60.9/73.8/**94.1** & 7.4/18.6/**36.6** \\ NV+SP+NN & 82.9/88.1/**93.9** & 41.1/57.3/**75.2** & 58.9/85.9/96.1 & 58.1/70.2/93.1 & 6.9/18.4/**32.9** \\ NV+SP+SG & 78.6/95.1/97.7 & 70.4/87.4/**98.6** & 65.4/88.4/**95.8** & 55.9/68.8/**93.7** & 6.8/18.8/**31.3** \\ HF-Net+NN & 75.6/87.9/**91.1** & 46.2/62.9/**79.6** & 55.4/86.5/**95.6** & 46.9/74.7/**95.9** & 3.6/7.1/**18.5** \\ HF-Net+SG & 87.4/93.2/**94.5** & 64.4/84.2/94.1 & 64.5/87.2/**96.2** & 45.9/67.2/**91.0** & 4.6/18.5/**26.9** \\ \hline \end{tabular}
\end{table} 
Table 3: Localization results of DLALoc with 100 anchors and 25 RANSAC sampling times. Figure 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**DLALoc with 100 Anchors and 100 RANSAC Sampling Times**} \\ \hline  & \multicolumn{2}{c}{**Aachen**} & \multicolumn{2}{c}{**RobotCar**} \\  & \multicolumn{2}{c}{**Day**} & \multicolumn{2}{c}{**Night**} & \multicolumn{2}{c}{**Dusk**} & \multicolumn{2}{c}{**Sun**} & \multicolumn{2}{c}{**Night**} \\
**Distance [m]** & 0.25/0.50/5.0 & 0.5/1.0/5.0 & 0.25/0.50/5.0 & 0.25/0.50/5.0 & 0.25/0.50/5.0 \\
**Orient. [deg]** & 2/5/10 & 2/5/10 & 2/5/10 & 2/5/10 & 2/5/10 \\ \hline NV+SIFT+NN & 85.1/88.6/94.7 & 45.6/58.2/76.4 & 58.4/87.2/95.3 & 51.2/72.3/93.0 & 8.2/14.2/27.4 \\ NV+SIFT+SG & 84.9/89.7/95.8 & 57.7/75.2/90.1 & 63.6/88.4/96.6 & 65.0/75.4/94.9 & 8.9/21.4/38.1 \\ NV+SP+NN & 86.7/89.9/94.2 & 42.8/59.4/77.6 & 60.5/86.8/96.3 & 60.3/73.6/94.2 & 7.9/20.4/35.1 \\ NV+SP+SG & 79.0/95.7/98.2 & 74.4/89.6/98.7 & 69.2/89.3/97.2 & 57.5/70.3/94.9 & 8.6/19.7/34.1 \\ HF-Net+NN & 74.6/91.5/94.8 & 56.6/69.3/94.2 & 60.6/89.9/96.9 & 51.2/78.4/97.2 & 4.7/9.3/21.5 \\ HF-Net+SG & 89.3/94.8/95.3 & 67.5/87.7/97.7 & 58.7/89.9/97.2 & 47.1/68.9/92.4 & 5.9/20.3/28.4 \\ \hline \end{tabular}
\end{table} 
Table 5: Localization results of DLALoc with 100 anchors and 100 RANSAC sampling times. Figure 6: Filtration effect of DLALoc method, from (**a**–**e**) are the correctly retrieved database images, and DLALoc can efficiently filter out the outliers.