# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 5 Experimental Evaluation
### Experimental Results and Analysis
(**f**) is the wrong retrieved database image, and DLALoc can filter out all mismatches. Table 6 compares the overall time of DLALoc with 100 anchors and 50 RANSAC sampling times and the time it spends in separate parts with the baseline of three combinations on the Aachen dataset. The total localization time of these three methods differs from 20.4 to 35.6 mins. The time difference between these three methods mainly concentrates on the feature matching and P3P steps. We also improve the algorithm by putting the feature correspondences produced by these methods into the MLP and using the MLP to filter the outliers. We can see the total time of the improved NV+SP+NN method reduced from 35.6 to 6 min. However, it almost achieves the localization accuracy on all three levels of the original NV+SP+SG method. Moreover, we can see the total time of the improved NV+SP+SG method reduced from 20.4 to 4.6 mins, and we can still improve the localization accuracy. Moreover, HF-Net seems to be a compromise of efficiency and accuracy, which has the shortest matching time in all three baselines and can achieve relatively high accuracy. With our improvement, this combination can achieve outstanding efficiency and state-of-the-art accuracy. As can be seen, our method has a significant time priority compared with the traditional method. This is mainly because the reduction of the P3P time is way beyond the additional time brought by the relative pose estimation and outlier filtration. The resulting P3P points are fewer; however, the 2D-3D matches are more robust. Although, some deep-learning matching strategies can provide more robust correspondences, they will bring additional space and time overhead. Meanwhile, we can use the fine filtration strategy to efficiently filter the outliers even with relatively poor matches as input, which can act as a substitute for expensive matching strategies. Moreover, from the 25 to 100 RANSAC sampling times, the additional time spent on calculating relative pose and filtering outliers is shorter than that of other algorithms. However, the total time for localization can be reduced significantly, and the accuracy can be improved from 50 RANSAC sampling times. Furthermore, we train the MLP with different numbers of anchors as output to illustrate how the performance of the DLALoc algorithm can react to different anchor numbers. Figure 7 shows the algorithms' accuracy and time on different anchor numbers. It can be clearly seen that the accuracy of calculating relative pose increases as the number of anchors increases. Interestingly, the runtime is not monotonic in the anchor number, and we can see the best performance occurs when the anchor number is 100. The reason is as follows. When the anchor is less than 100, the outliers cannot be effectively filtered out yet, so the 2D-3D matching pairs for localization still contain some outliers. When anchors are larger than 100, the filtration performance of MLP confronts a bottleneck and can be no longer improved, so the P3P time cannot be reduced, whereas the calculation time of MLP increases as anchors increase. Nevertheless, for all anchor numbers, the localization time is significantly less than the baseline time. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Matching** & **P3P Time** & **MLP Time** & **Total Time** & **Improvement** & **Local Features** & **P3P Points** \\  & **Time (min)** & **(min)** & **(min)** & **(min)** & **Improvement** & **Local Features** & **P3P Points** \\ \hline NV+SP+NN & 64 & 35.6 & - & 35.6 & - & 2492 & 2096 \\ DLALoc(NV+SP+NN) & 64 & **5** & 0.5 & **5.5** & **84.5\%** & 2492 & **746** \\ NV+SP+SG & 258 & 20.4 & - & 20.4 & - & 1764 & 1657 \\ DLALoc(NV+SP+SG) & 258 & **4.1** & 0.5 & **4.6** & **76.7\%** & 1764 & **1032** \\ HF-Net+NN & 61 & 25.6 & - & 25.6 & - & 1432 & 1324 \\ DLALoc(HF-Net+NN) & 61 & **4.3** & 0.5 & **4.8** & **81.3\%** & 1432 & **998** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time and number of P3P points comparison of DLALoc with baseline methods.