# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### In-Depth Analysis of the Proposed Method
#### 3.3.2 The Advantage of Learning Mechanism  
To further explore the performance difference between Proposed-A and -B during the evolution, an additional metric is designed as  
\[\overline{a}_{e}(i,*)=\frac{1}{|Data|}{\sum_{Data}\frac{1}{|*|}{\sum_{*}a_{e}(i)},*=\mathcal{P}_{i}\,\,or\,\,\mathcal{P}_{i}^{\prime}} \tag{24}\]  
where \(\mathcal{P}_{i}\) and \(\mathcal{P}_{i}^{\prime}\) represent the \(i\)-th round of parents and the migrated parents. Then, let \(\overline{a}_{e}(i,*)\) be the mean of \(\overline{a}_{e}(i,*)\) for all repetitions (\(\overline{t}_{e}(i,*)\) is the same), and the results are shown in Figure 10.  
Overall, the final performance and improvement speed of \(\overline{\overline{a}}_{e}\) in -B is significantly higher than that of -A, which indicates that a more reasonable parameter reduction approach is conducive to the high-quality update of \(\mathcal{F}\), indirectly increasing the probability of local difficult components escaping the dilemma. Comparing \(\overline{\overline{a}}_{e},\overline{\overline{t}}_{e}\) with their respective \(\overline{\overline{a}}_{e}(i,*)\), \(\overline{t}_{e}(i,*)\), it can be observed that appropriate reproductive strategy can provide suitable local exploration for the composition of the optimal viewpoint and make \  
local exploration requires a reasonable combination with global exploration, so blindly expanding \(C\) will inject too many low-quality attempts, harming the updating of \(\mathcal{F}\) and the evolution process. In terms of process, excessive \(C\) makes it difficult to maintain the early advantages of evolution and premature convergence to the low-performance range. When \(C\) is fixed, the performance gradually stabilizes with the increase in the \(P\). However, from a process perspective, although the results have improved, more fluctuations have emerged during this period, resulting in delayed convergence. To balance accuracy and efficiency, \(P10C5\) was adopted for subsequent experiments. Letting \(s_{0}\in\{0.15,0.125,0.1,0.075,0.05\}\), \(\tau_{0}\in\{35^{\circ},30^{\circ},25^{\circ},20^{\circ},15^{\circ}\},\lambda_ {0}\in\{0.9,0.8,0.7,\allowbreak 0.6,0.5\},\alpha_{0}\in\{0.6,0.5,0.4,0.3,0.2\},\sigma_{0}\in\{35,30,25,20,15\}\), the orthogonal experiments with 5 factors and 5 levels were performed, and the results are shown in Table 3.  
Moreover, to further clarify the impact of each factor, we drew a corresponding 5-factor 5-level main effect plot based on Table 3. For ease of calculation, the pose error was merged as \(norm(\overline{\overline{a}}_{e})+norm(\overline{\overline{t}}_{e})\), and the results are provided in Figure 12. In terms of value range (the first row), it can be found that \(\{\lambda_{0},\sigma_{0}\}\) have a strong influence on the performance (0.3597, 0.3940) and tend to provide the best results at levels 4 and 5. This is because \(\lambda_{0}\) is a variable that controls the degree of global exploration and \(\sigma_{0}\) determines the search scope of the viewpoint quality evaluation. With the assistance of  
Figure 11: The influence of the number of parents and children on the performance. (**a**) Pose performance during evolution. (**b**) Skews (\(\overline{\mathit{ToU}}_{e}\)) under different configurations when \(iter=10\) and \(iter=20\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Config** & \(\overset{=}{a_{e}}\) (\({}^{\circ}\)) & \(\overset{=}{t_{e}}\)**(mm)** & **Config** & \(\overset{=}{a_{e}}\) (\({}^{\circ}\)) & \(\overset{=}{t_{e}}\)**(mm)** \\ \hline \hline
**\{1,1,1,1,1}** & 0.7118 & 38.6272 & **\{3,4,5,1,2}** & 0.3289 & 31.6075 \\ \hline
**\{1,2,3,4,5}** & 0.5432 & 30.9155 & **\{3,5,2,4,1}** & 0.6245 & 31.4838 \\ \hline
**\{1,3,5,2,4}** & 0.5148 & 32.5514 & **\{4,1,3,5,2}** & 1.0717 & 40.5826 \\ \hline
**\{1,4,2,5,3}** & 0.4505 & 30.4453 & **\{4,2,5,3,1}** & 0.3486 & 25.7130 \\ \hline
**\{1,5,4,3,2}** & 0.5673 & 32.5823 & **the method mechanism, an appropriate increase in \(\{\lambda_{0},\sigma_{0}\}\) can indeed contribute to early exploration.