# ALReg: Registration of 3D Point Clouds Using Active Learning
## 4 Experiments
Also, all methods outperformed the full train scenario, which demonstrates that using all points leads to confusion for the network. Evaluating the network on 3 DMatch, \(ALReg_{UNC}\) method still outperforms random selection. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & & & & & & \\ \hline
**Method** & **MSE (R)** & **RMSE (R)** & **MAE (R)** & **MSE (t)** & **RMSE (t)** & **MAE (t)** \\ \hline FULL & 0.39343 & 0.62724 & 0.25740 & 0.00614 & 0.07835 & 0.03035 \\ \hline RAND & 0.2110 \(\pm\) 0.0308 & 0.4581 \(\pm\) 0.0344 & 0.1461 \(\pm\) 0.0211 & 0.0036 \(\pm\) 0.0009 & 0.0594 \(\pm\) 0.0080 & 0.0181 \(\pm\) 0.0038 \\ \(ALReg_{DIV}\) & 0.1524 \(\pm\) 0.0551 & 0.3842 \(\pm\) 0.0692 & 0.1196 \(\pm\) 0.0308 & 0.0131 \(\pm\) 0.0162 & 0.0471 \(\pm\) 0.0099 & 0.0152 \(\pm\) 0.0041 \\ \(ALReg_{UNC}\) & **0.1463 \(\pm\) 0.0203** & **0.3816 \(\pm\) 0.0266** & **0.1167 \(\pm\) 0.0143** & **0.0021 \(\pm\) 0.0002** & **0.0453 \(\pm\) 0.0018** & **0.0139 \(\pm\) 0.0011** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FMR training results for 7 scene and 3 DMatch datasets. ALReg method is also compared with original full-size registration. Figure 3: Randomly created superpoints for ModelNet40 dataset. In Figure 4, the superpoints actively selected by the different methods are visualized. Qualitatively, both \(ALReg_{DIV}\) and \(ALReg_{UNC}\) selected a more representative set compared to the random selection. In fact, there are many common superpoints. Considering the locations of these superpoints, it could be asserted that, selecting superpoints from the borders is a better approach than selecting randomly. We also evaluated ALReg's performance on FMR according to the amount of rotation starting from [0, 0, 0] to \([\frac{\pi}{2},\frac{\pi}{2},\frac{\pi}{2}]\). According to Figure 5, even though their metric performances are worse, it is observed that for larger rotations on 3 DMatch, both ALReg methods provide reduced errors than the full point cloud training. For DCP and DeepBBS methods, the numerical results are given in Table 2. According to the results, our \(ALReg_{UNC}\) performs slightly behind performance than the full train procedure in all cases. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**DCP-ModelNet40**} \\ \hline
**Method** & **MSE (R)** & **RMSE (0)** & **MAE (0)** & **MSE (0)** & **RMSE (0)** & **MAE (0)** \\ \hline FULL & 1.3073 & 1.1433 & 0.7705 & 0.0000 & 0.0017 & 0.0011 \\ \hline RAND & 2.373 \(\pm\) 0.119 & 1.5255 \(\pm\) 0.024 & 1.0056 \(\pm\) 0.012 & 0.0000 \(\pm\) 5.61 \(\times\) 10\({}^{-9}\) & 0.0003 \(\pm\) 3.28 \(\times\) 10\({}^{-5}\) & 0.0023 \(\pm\) 2.31 \(\times\) 10\({}^{-5}\) \\ \(ALReg_{DIV}\) & 1.3607 \(\pm\) 7.78 \(\times\) 10\({}^{-4}\) & 1.4027 \(\pm\) 2.75 \(\times\) 10\({}^{-4}\) & 1.4027 \(\pm\) 5.11 \(\times\) 10\({}^{-3}\) & 0.0000 \(\pm\) 1.44 \(\times\) 10\({}^{-7}\) & 0.0034 \(\pm\) 8.97 \(\times\) 10\({}^{-9}\) & 0.0023 \(\pm\) 1.31 \(\times\) 10\({}^{-6}\) \\ \(ALReg_{UNC}\) & **1.2817 \(\pm\) 0.127** & **1.3497 \(\pm\) 0.026** & **0.9474 \(\pm\) 0.0133** & **0.0000 \(\pm\) 4.32 \(\times\) 10\({}^{-9}\)** & **0.0002 \(\pm\) 3.28 \(\times\) 10\({}^{-5}\)** & **0.0022 \(\pm\) 2.27 \(\times\) 10\({}^{-5}\)** \\ \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**DeepBBS-ModelNet40**} \\ \hline
**Method** & **MSE (R)** & **MAE (0)** & **MSE (0)** & **RMSE (0)** & **MAE (0)** \\ \hline FULL & 1.67 \(\times\) 10\({}^{-8}\) & 1.29 \(\times\) 10\({}^{-3}\) & 5.18 \(\times\) 10\({}^{-4}\) & 1.16 \(\times\) 10\({}^{-10}\) & 1.07 \(\times\) 10\({}^{-5}\) & 6.55 \(\times\) 10\({}^{-6}\) \\ \hline RAND & 2.39 \(\times\) 10\({}^{-8}\) & 1.27 \(  
selection mechanism, highly beneficial point cloud parts could be selected compared to random selection. Moreover, it can be thought that using only a few point cloud parts hinders the robustness of the model.