# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### Performance of Pose Estimation  
Three commonly used solutions in APE were employed for comparison, including direct estimation, iterative estimation, and hierarchical approach. Each solution was supported by a different combination of the most common and classic algorithms. Specifically, the methods for virtual-real feature matching consist of nearest neighbor principle (S0), normal-based search [14] (S1), fusion of Turkey estimation and color [45] (S2), region-based method [16] (S3), iterative point matching [39] (S4), and global strategy FRICP [46]. The optimized part can be arbitrarily selected from EPnP [20], EPnP, CEPnP [22], ASPnP [31], PPnP [23], and MLPnP [24]. DL-based methods employed in hierarchical strategy include PoseNet2 [29], single-shot pose [47], and state-of-the-art estimator PVNet [30] and MegaPose [34]. Another part of the latest study [31; 32] has also undergone research. However, in the case of insufficient sample size/form and dense auxiliary supervision signals, these structurally complex networks are difficult to train sufficiently and prone to overfitting. Therefore, considering the different assumptions of the research, they were not compared for the sake of fairness. The control group settings are as follows: (a) Control group I. The virtual viewpoint sampling and matching method described in Section 2 was employed to perform the rough positioning. Only the direct estimation scheme with the best performance is listed. (b) Control group II. Iterative version of Group I. For each optimization algorithm, the  
Figure 7: Practical application, data preparation, and configuration. combination with the best performance and the combination with the method in Section 2.1 (VPS) were compared. (c) Control group III. Different feature-matching algorithms were embedded into the proposed viewpoint evolution. The group applying the deep learning method was denoted as Group DL, which includes the following: (a) DL-A. The rough pose was adjusted by PoseNet2 with a different number of bounding box labels. The bounding boxes of two devices from Range I were set as labels in DL-A1, and the same 2 boxes from Range I, together with another 4 boxes of devices from Range II, were set as labels in Group DL-A2. (b) DL-B. The single-shot pose network was trained with the labels of DL-A1, and its output was used for PnP estimation to obtain the rough pose. The label was modified from a bounding box to an 8-point bounding box. (c) DL-C. The estimated result of DL-B was adopted as the rough pose for Group II; DL-C1 represents the combination with best performance, and DL-C2 represents the combination with VPS. (d) DL-D. PVNet is employed, whose semantic labels were generated from the minimum bounding rectangle of significant geometric features. (e) DL-E. MegaPose is selected, where the base is removed from the 3D model, and the bounding box prompt is manually set. For all DL Groups A-D, about 120 real samples and 1200 virtual samples were prepared for model training. The configurations of the proposed method were as follows: (a) Proposed-A. Evolution function learning was disabled in this group, while only one rendering according to the rough pose was allowed. (b) Proposed-B. Evolution function learning and multiple rendering were enabled in this group, and the estimated result of DL-B was accepted as a rough pose. The results are listed in Table 1, and the performance is shown in Figure 8. The areas that can reflect the difference in accuracy are marked, and the higher the brightness, the more accurate. Overall, it can be summarized as the following points: (a) the effectiveness of the proposed method can be proved; (b) non-DL methods are still the first choice for high-precision applications; (c) DL module with weak annotation can be used to provide a suitable initial pose under insufficient dataset; (d) the possibility of escaping from the local optimum in feature matching is the main factor affecting the performance.