# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### Appearance Difference Adaptability  
This section verified the adaptability of the proposed method to the appearance difference (e.g., product at different stages or lightweight model) and viewpoint difference between the virtual model and real product. The setting of appearance difference is shown in Figure 13, where A, D, and E were not equipped with cables, one device belonging to Range II was removed in B1, one virtual component belonging to Range II was removed in B2, and C = B1 + B2. Additionally, the visual style of Groups A-C was different from the standard, and the part dimensions of Groups D and E were modified, as shown in Figure 13, respectively. The dataset was adjusted according to the above changes, and the experimental results are listed in Table 4.  
The results proved the adaptability of the proposed method to state differences, especially to visual feature changes caused by the differences in model rendering style.  
Figure 12: Main effect of performance. The red circle indicates the potential level of factors.  
Figure 13: Configuration of state difference between 3D model and real product.  
Moreover, pose errors of Groups A, D, and E reflect that sparse context changes such as component size modification have less impact on the performance than rendering style changes. Although the error of Group C is close to 0.75, 69.59 mm, the proposed method still has an advantage over the existing methods listed in Table 1. The impact of a missing part is significant, and the reasons are shown in Figure 14. The current object has a high similarity with the component behind it in this viewpoint, and mismatching is considered a high-quality correspondence. Moreover, when occluded components participate in matching, their features are extremely different from the region that contains a missing part but rather highly similar to the noise. Therefore, the coupling of the above situations prevents anomalies from being intercepted by VPS, causing viewpoints that should have been discarded to always participate in feasible region construction and evolution, resulting in continuous accumulation of errors.  
The viewpoint difference was adjusted by setting the total rendering times of the evolution process. For example, Group 2 means that only the first two rounds enabled rendering. The virtual image rendering was performed based on the viewpoint with the best \(vp^{q}\) in each round. The upper limit of iteration rounds was set to 11, and the results are shown in Table 5. By comparing the results of Group 1 in Table 5 with those of Proposed-A in Section 3.2, it can be further found that the value of evolution function optimization. The results of Groups 1-3 prove the adaptability of the proposed method to the differences between virtual and real viewpoints and the geometric feature inconsistency caused by them. The pose estimation accuracy is positively correlated with the rendering times. By observing the change in \(\overline{iter}\) from Groups 4 to 11, we can find that rendering times are not the more, the better. Since suitable results usually appear in the 6 th to 9 th rounds and then tend to be stable, the difference between virtual and real features brought by additional rendering will stimulate more exploration, which will lead to fluctuation in results and an increase in \(\overline{iter}\). Therefore, the balance between accuracy and performance should be determined based on practical applications.