# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 1 Introduction  
In the high-end equipment manufacturing industry, accurate multi-view pose estimation of complex products is an essential step for subsequent appearance quality inspection. Taking an aerospace product as an example, a piece of equipment may consist of multiple sub-modules, including dozens or even hundreds of intricate parts (complex assembled products). To ensure integrity and reliability, the product needs to be repeatedly compared with 3D models and process documents from different viewpoints to cover all visible elements to avoid defects during its assembly stage. The complexity of structure and requirements to make this work are still completed manually in most cases. In this context, if there is a method to obtain the pose of the current viewpoint, it will greatly improve the efficiency of the process. Based on the estimated pose, the 3D model can be adjusted to the same perspective as the real object so as to extract the corresponding region of interest (ROI) and filter irrelevant context [1; 2]. In such a way, the reference provided by a virtual image can greatly simplify inspection and evaluation (whether it is for theoperator or algorithm) while avoiding the difficulty of building an end-to-end multi-target detector. The inherent logic of pose estimation is to solve the problem of constructing a virtual-real feature connection in a scenario with diverse noise and continuously adjust incorrect correspondences through correct correspondences (e.g., feature point). For vision technology that utilizes sensor perception and algorithm judgment, this seemingly simple task for humans is quite difficult, and the challenges can be summarized as follows:  
(1) Industry characteristics: In the realm of high-end equipment manufacturing, the interpretability and dependability of methods are important considerations. Although it is possible to train a specific neural network for pose estimation through a large amount of data, blindly adhering to the big data-driven learning paradigm in the pursuit of adaptability may render it challenging to address failures, abnormalities, and re-improvement, ultimately impeding promotion [3]. (2) Task features: These assembled products with high structural complexity may involve dozens or even hundreds of components, typically assembled through human or human-machine collaboration. Under such operation scenarios, it is difficult to collect enough high-quality samples at once or accurately pre-plan the imaging pose. The task features of a small sample, multiple components, and coarse initial pose greatly increase the difficulty of pose estimation/adjustment in common vision-based applications, e.g., AR/VR-based projection [4] mobile robots based on viewpoint pre-planning [5; 6]. (3) Product appearance: As shown in Figure 1, the appearance features of the target and background captured from different viewpoints are quite different, and the multi-layer stacking between objects will further enhance the difference. Due to perspective effect and distortion, whether the extremely small-sized target located at the far end can be successfully recognized is sensitive to the accuracy of pose estimation. It is common that there are differences in features and visual styles between the process model and physical object. Additionally, the surface treatment of machined products can result in sparse texture and high color consistency that are not conducive to feature extraction. Technically, camera pose estimation focused in this study is the absolute pose estimation (APE). Research in this direction mainly includes six categories [7; 8; 9]:  
(1) APE based on cooperative target can be further subdivided into manual markers [10; 11], auxiliary projection [12], laser tracker [13], and structured light measurement [14]. In these methods, easily identifiable features are supplemented to the target manually or by automated devices to alleviate the difficulty of feature extraction and analysis during APE.