# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 1 Introduction
This strategy is commonly used in high-precision tasks, such as calibration, and is also suitable for large structural parts with smooth surfaces and simple structures. Furthermore, with the support of a movable robot vision platform [15], APE can be transformed into matching and stitching of a 3D point cloud. (2) APE based on geometric features includes two steps: feature extraction and relationship construction of 2D pixels and 3D coordinates (2D-D) and optimization-based APE estimation. Manual feature [16; 17; 18] and learning-based approaches [19] are usually adopted  
Figure 1: Difficulties of high-precision camera pose estimation for complex assembled products. in the first step, in which two key issues need to be considered, namely, the adaptability of 2D features and the robustness of 2D-3D relationship evaluation. The representative optimization method for APE estimation is EPnP [20] and some other related studies [21; 22; 23; 24]. In the estimation stage, it is essential to reduce the interference of outliers on the 2D-3D relationship and avoid getting trapped in the wrong local optimal solution. Compared with other technical routes, strategy (2) can obtain the best APE accuracy, but it is limited by the complexity of the scene to obtain enough 2D-3D pairs with correct relations at one time. Moreover, the iterative mode is easily affected by the latest observations, making the final convergent result significantly biased. (3) APE based on mapping and retrieval is one of the commonly used strategies in the field of simultaneous localization and mapping (SLAM), emphasizing the robustness of scene feature encoding and the ability to describe differences between different viewpoints [25; 26]. When the accuracy requirement is not high, the pose can be obtained by retrieving the viewpoint closest to the current feature in the map library; otherwise, the result of strategy (3) will be adopted as the initial value for strategy (2) [27]. (4) APE based on deep learning (DL) relies on a deep convolutional neural network (CNN) to perform pose estimation in an end-to-end manner. CNN-based architectures represented by classic research PoseNet series [28; 29] do not require feature engineering and have a strong adaptability to feature variation. Nevertheless, there is still a gap in reliability and accuracy between strategy (4) and strategy (2) [30]. Moreover, some state-of-the-art studies indicate that close performance can be achieved in public datasets such as LineMod, LineMod-O [31; 32; 33], and MegaPose [34] and can adapt to unseen objects through rendering and comparison, but they all require ensuring model accuracy, fewer targets, and balanced sizes, which is significantly different from the complex assembled product. (5) Hybrid and hierarchical strategies include methods based on functional hybrid training of multiple CNN models [35; 36] and hierarchical approximation from coarse to fine [37]. Research in strategy (5) is devoted to addressing the adaptability of pose estimation to changeable feature context in open multi-scene. The accuracy achieved depends partly on the architecture and trick design of CNN and partly on the geometric feature-based APE algorithm adopted in the backend. Through the literature review above, it can be found that the compatibility of accuracy and adaptability in pose estimation of complex assembled products under free view hinges on building an interpretable hierarchical architecture while maintaining the advantages of empirical design and learning paradigm. However, the interpretability and consistency of DL-based methods limit their application in manufacturing, while traditional APE lacks local-global collaborative control in matching and optimization mechanisms and relies on fixed empirical parameters in adaptability, resulting in quite strict requirements for initial pose in complex assembled products.