# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 4 Discussion and Conclusions  
In this study, we present a novel attempt at the integration of knowledge-based agent models and data-driven deep learning for accurate pose estimation of complex products. The pose estimation is reconstructed as an evolution process. To solve the problem of large-scale solution space optimization, virtual-real matching considering structural stability, evolvable feasible region constraint, and adaptive population migration and reproduction are proposed. To reduce the empirical dependency in parameter tuning rules, enhance robustness and promote rapid convergence, a guided-model-based hierarchical cyclic effective trajectory learning mechanism is proposed and embedded. Compared with the best result 1.28\({}^{\circ}\), 77.67 mm and 1.69\({}^{\circ}\), 55.67 mm in classic and latest solutions under different existing method configurations, the proposed method enjoys better performance, the pose error can reach 0.23\({}^{\circ}\), 23.71 mm, and the cause of abnormal results can be retraced.  
Moreover, through experiments and analysis, we have found the following:  
Figure 15: Settings and application cases in real scenarios. (**a**) The components are divided according to size and participate in pose estimation based on this order. (**b**) Pose estimation performance. The blue points represent the reprojection effect of virtual features.  
1. A hierarchical architecture from coarse to fine is a practical approach to image-based pose estimation while maintaining high accuracy and reliability;
2. Consistent with previous studies [28; 29; 30; 35], cascaded DL module and geometric optimization can effectively balance adaptability and accuracy;
3. Some practical problems, such as limited samples, complex structure, and multiple elements, make it difficult to directly perform complex advanced DL-based networks. On the contrary, indirect models that only require weak annotation are easier to deploy in practical applications;
4. For global or divide-and-conquer architectures based on matching, metric, and optimization, the coordination of exploration and optimization requires more detailed regulation in the pose estimation of complex assembled products.  
From the perspective of application value, through case studies and validation, it shows that the method will be beneficial for non-serious measurement applications that require camera pose in the assembly process of some complex products, such as AR projection, robot-assisted control, appearance inspection, etc.  
In terms of potential limitations, it is undeniable that many specialized designs and appropriate initial poses need to be ensured. In practical applications, we recommend planning the imaging pose network or employing robot imaging for known scenarios in advance to reduce the search space. Moreover, we plan to carry out further validation in more manufacturing scenarios to identify more possible practical challenges (e.g., actual missing parts) and adopt CNN-based strategies to represent feasible regions.  
Conceptualization, D.Z.; methodology, D.Z.; software, D.Z. and F.K.; validation, D.Z. and F.K.; formal analysis, D.Z. and F.K.; investigation, D.Z. and F.D.; resources, F.D.; data curation, F.K.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z.; visualization, D.Z. and F.K.; supervision, F.D.; project administration, F.D.; funding acquisition, F.D. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the National Natural Science Foundation of China (grant number 52375478).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.