## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \
\begin{tabular}{c} **King’s** \\ **College** \\ \end{tabular} & **Old Hospital** & **Shop Facade** &
\begin{tabular}{c} **St Mary’s** \\ **Church** \\ \end{tabular} \\ \hline \multirow{2}{*}{Case 1} & Average IOU & 0.94 & 0.91 & 0.90 & 0.90 \\  & Point Detection Precision & 0.65 & 0.47 & 0.55 & 0.51 \\ \hline \multirow{2}{*}{Case 2} & Average IOU & 0.93 & 0.92 & 0.92 & 0.89 \\  & Point Detection Precision & 0.71 & 0.59 & 0.65 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of point detection performance by scene and experimental configuration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \begin{tabular}{c} **King’s** \\ **College** \\ \end{tabular} & \begin{tabular}{c} **Old** \\ **Hospital** \\ \end{tabular} & \begin{tabular}{c} **Shop** \\ **Fagade** \\ \end{tabular} & \begin{tabular}{c} **St Mary’s** \\ **Church** \\ \end{tabular} & **Heads** \\ \hline \multirow{2}{*}{PoseNet [3]} & Rotation (\({}^{\circ}\)) & 5.40 & 5.38 & 8.08 & 8.48 & 12.0 \\  & Translation (m) & 1.92 & 2.31 & 1.46 & 2.65 & 0.29 \\ \hline \multirow{2}{*}{LSTM-Pose [32]} & Rotation (\({}^{\circ}\)) & 3.65 & 4.29 & 7.44 & 6.68 & 13.7 \\  & Translation (m) & 0.99 & 1.51 & 1.18 & 1.52 & 0.21 \\ \hline \multirow{2}{*}{PoseNet2 [33]} & Rotation (\({}^{\circ}\)) & 1.04 & 3.29 & 3.78 & 3.32 & 13.0 \\  & Translation (m) & 0.88 & 3.20 & 0.88 & 1.57 & 0.17 \\ \hline \multirow{2}{*}{VLocNet [34]} & Rotation (\({}^{\circ}\)) & 1.42 & 2.41 & 3.53 & 3.91 & 6.64 \\  & Translation (m) & 0.84 & 1.08 & 0.59 & 0.63 & 0.05 \\ \hline \multirow{2}{*}{Active Search [6]} & Rotation (\({}^{\circ}\)) & 0.6 & 1.0 & 0.4 & 0.5 & 1.5 \\  & Translation (m) & 0.42 & 0.44 & 0.12 & 0.19 & 0.02 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} AdapNet+SuperPoint \\ (Case 1) \\ \end{tabular} } & Rotation (\({}^{\circ}\)) & 1.42 & 3.39 & 1.68 & 1.69 & 2.86 \\  & Translation (m) & 0.62 & 0.96 & 0.20 & 0.49 & 0.74 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} AdapNet+SuperPoint \\ (Case 2) \\ \end{tabular} } & Rotation (\({}^{\circ}\)) & 1.26 & 2.85 & 2.00 & 1.62 & - \\  & Translation (m) & 0.61 & 0.77 & 0.19 & 0.50 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of translation and rotation errors observed with different camera pose estimation methods. A comparison of the duration of each computation step by scene and experimental case is included in Table 4. The results above have shown that with our voxel-based scene representation, the number of voxels affects computation performance. This is also reflected in Table 4. Representing the scene with more voxels decreases the average area of a voxel region. As such, fewer 3D points are included in a voxel region, and both the number of interest points and the size of the representative description information vector are decreased. As a result, the number of candidate pairs considered in the description matching procedure is reduced, such that the duration of this process, as well as the pose estimation procedures, is reduced, as shown in Table 4. Conversely, since the number of voxels in the scene increases, the number of inlier voxels also increases. Hence, in general, the number of voxel regions to be classified in an image increase. In addition, AdapNet's weight parameters are also made larger, as this is required to classify more voxel classes. The combination of these effects means that it takes much more time to detect the voxel regions in images with Case 2 than with Case 1. Hence, since voxel region detection takes up more than half of the total computation time, in general, computation is faster with Case 1 than with Case 2.