# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### Performance of Pose Estimation
The difficulty of the problem is evident in Figure 8a, and the results of Group I and the reasons are shown in Figure 8b. Unpredictable background diversity and various overlapping states pose challenges to the construction of correspondence in the matching process under dense noise, while virtual-real geometric difference further increases the difficulty of noise identification. Moreover, under some special initial viewpoints, there is an uneven deviation in the virtual-real correspondence, which makes it more difficult to explore the correct direction in local large-deviation areas with dense noise. Take the upper plug in the sixth row of the sub-image as an example. There is a large spatial difference between the plug and all visible components that provide the 2D-3D pairs for pose estimation. Therefore, even if these visible components are well positioned, the \(\overline{ToU_{e}}\) of the plug may still be less than 0.5. Through the results of Screws \(\overline{ToU_{e}}\), it can be proved that a satisfactory camera pose cannot be achieved only by direct estimation. Since the local iteration mode of S4 brings more exploration to the virtual-real feature matching of each component, it shows the best performance and is listed in Table 1.  
In terms of matching strategy, S3, which simultaneously fuses edge, texture, and color, is the best choice among S0-S4. The matching and weight adjustment of S4 is based on S0, so it is easily interfered with by noise and has no advantage in Group II. Compared with S0-S4, the strategies of structural stability and advantage direction in VPS effectively improve feature matching, especially in the reduction in angle error. From the perspective of the matching mechanism, the conventional iterative mode lacks exploration and cannot be solved by simply increasing the search range. Once the components with sparse texture stack seriously and the initial deviation is large, the matching process is easily affected, which further leads to a local optimal result (e.g., Figure 8b). Therefore, as shown in the hard example in Figure 8a, no matter how many iterations, there is no obvious difference between the obtained pose and the rough pose. Similarly, iterative global matching tendsto maintain the majority of good (minor deviation) while sacrificing bad (large deviation), resulting in cumulative deviation. On the other hand, the applied optimization methods usually assume that the noise follows a Gaussian distribution in their studies. Therefore, it is possible for the method to ignore outliers and escape from the local optimal state only when the distribution of noises in 2D-3D is close to the Gaussian assumption. In practice, a more common situation is that the final matching results tend to exhibit a biased state due to ignoring the support of geometric features in low-quality matching regions. For example, as shown in the result of Group II in row 5 and row 2, the projection result is skewed by the estimated pose to the side with suitable 2D-3D pairs. According to the results shown by the combination with VPS, the performance difference between the optimization algorithms is not obvious.