# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### Performance of Pose Estimation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \begin{tabular}{c} **Range I** \\ \(\mathbf{(IoU_{e})}\) \\ \end{tabular} & \begin{tabular}{c} **Range II** \\ \(\mathbf{(IoU_{e})}\) \\ \end{tabular} & \begin{tabular}{c} **Range III** \\ \(\mathbf{(IoU_{e})}\) \\ \end{tabular} & \begin{tabular}{c} **Plugs** \\ \(\mathbf{(IoU_{e})}\) \\ \end{tabular} & \begin{tabular}{c} **Screws** \\ \(\mathbf{(IoU_{e})}\) \\ \end{tabular} & \begin{tabular}{c} **Average Error** \\ \(\overline{u}_{e}\) (\({}^{\circ}\)) \\ \end{tabular} & \begin{tabular}{c} **\(\overline{t}_{e}\)(mm)** \\ \end{tabular} \\ \hline \hline \multicolumn{2}{c}{Rough pose} & **0.64** & **0.57** & **0.34** & **0.11** & **0.02** & **6.03** & **185.71** \\ \hline Group I & S4 + ASPnP & **0.74** & **0.60** & **0.43** & **0.13** & **0.03** & **3.61** & **134.74** \\ \hline \multirow{5}{*}{\begin{tabular}{c} EPnP \\ \end{tabular} } & FRICP + EPnP & 0.77 & 0.63 & 0.48 & 0.15 & 0.04 & 1.89 & 110.77 \\ \cline{2-9}  & S3 & 0.81 & 0.69 & 0.53 & 0.22 & 0.08 & 1.83 & 96.92 \\ \cline{2-9}  & VPS & 0.86 & 0.75 & 0.62 & 0.32 & 0.18 & 1.44 & 85.23 \\ \cline{2-9}  & CEPnP & S3 & 0.79 & 0.66 & 0.51 & 0.19 & 0.06 & 1.95 & 98.20 \\ \cline{2-9}  & VPS & 0.86 & 0.75 & 0.62 & 0.32 & 0.18 & 1.44 & 86.07 \\ \cline{2-9}  & RPnP & S3 & 0.77 & 0.64 & 0.49 & 0.17 & 0.04 & 2.02 & 102.32 \\ \cline{2-9}  & VPS & 0.86 & 0.75 & 0.62 & 0.31 & 0.18 & 1.43 & 86.29 \\ \cline{2-9}  & S3 & **0.82** & **0.74** & **0.62** & **0.29** & **0.12** & **1.64** & **90.67** \\ \cline{2-9}  & VPS & **0.87** & **0.75** & **0.63** & **0.33** & **0.19** & **1.39** & **84.86** \\ \cline{2-9}  & EPPnP & S3 & 0.84 & 0.75 & 0.61 & 0.28 & 0.12 & 1.59 & 92.45 \\ \cline{2-9}  & VPS & 0.87 & 0.75 & 0.62 & 0.32 & 0.19 & 1.42 & 83.29 \\ \cline{2-9}  & MLPnP & S3 & 0.81 & 0.71 & 0.55 & 0.23 & 0.09 & 1.73 & 95.10 \\ \cline{2-9}  & VPS & 0.86 & 0.75 & 0.62 & 0.32 & 0.18 & 1.41 & 85.44 \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} Group III \\ \end{tabular} } & S0 & 0.89 & 0.81 & 0.72 & 0.49 & 0.30 & 0.97 & 63.41 \\ \cline{2-9}  & S1 & 0.92 & 0.87 & 0.79 & 0.60 & 0.49 & 0.63 & 47.90 \\ \cline{2-9}  & S2 & 0.92 & 0.87 & 0.80 & 0.60 & 0.47 & 0.71 & 47.33 \\ \cline{2-9}  & S3 & 0.93 & 0.89 & 0.83 & 0.65 & 0.51 & 0.59 & 44.78 \\ \cline{2-9}  & S4 & **0.97** & **0.95** & **0.90** & **0.81** & **0.65** & **0.26** & **33.05** \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Group \\ DL \\ \end{tabular} } & DL-A1 & 0.77 & 0.65 & 0.46 & 0.15 & 0.03 & 2.14 & 115.67 \\ \cline{2-9}  & DL-A2 & 0.79 & 0.67 & 0.50 & 0.19 & 0.05 & 1.93 & 103.52 \\ \cline{2-9}  & DL-B & 0.68 & 0.57 & 0.35 & 0.12 & 0.02 & 5.32 & 167.42 \\ \cline{2-9}  & DL-C1 & 0.85 & 0.75 & 0.61 & 0.32 & 0.15 & 1.57 & 84.18 \\ \cline{2-9}  & DL-C2 & **0.88** & **0.81** & **0.65** & **0.38** & **0.22** & **1.28** & **77.67** \\ \cline{2-9}  & DL-D & 0.85 & 0.74 & 0.60 & 0.29 & 0.11 & 1.79 & 89.45 \\ \cline{2-9}  & DL-E & 0.88 & 0.77 & 0.61 & 0.34 & 0.19 & 1.69 & 55.67 \\ \hline \multicolumn{2}{c}{Proposed-A} & **0.97** & **0.95** & **0.89** & **0.79** & **0.63** & **0.27** & **35.24** \\ \hline \multicolumn{2}{c}{Proposed-B} & **0.98** & **0.97** & **0.94** & **0.85** & **0.71** & **0.23** & **23.71** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Camera pose estimation performance comparison. The results of Group III demonstrate the robustness and improved ability of the proposed method to feature-matching algorithms. Due to the iterative local exploration ability, S4 in Group III shows excellent performance, even surpassing Proposed-A. However, this approach is less efficient and has insufficient practical significance. For the DL group, the amount of dataset is the primary issue. From the results of DL-A1 and A2, DL models always tend to provide suitable results near fully trained viewpoints, while its estimation performance degrades greatly when the target participating in training is occluded. Therefore, the improvement in the DL-A2 with more labels is not significant compared with DL-A1.