## 4 Experiment and Evaluation
### Correlation Analysis Between Pose Estimation and CAD Retrieval  
This study addresses not only the 6-DoF pose estimation of RGB objects but also CAD model retrieval for the purpose of AR and XR applications. Thus, it is expected that the performance enhancement of the proposed pose estimation positively affects the performance of the CAD retrieval. In this section, we investigate qualitative and quantitative analysis to verify the positive relationship between the performance of the two tasks. First, we evaluate the performance relationship qualitatively. In this evaluation, we visually compare 2D rendered images of the matching CAD model with the RGB object. If the predicted pose perfectly matches with the RGB object, then the pose appearance should be very similar. The visual comparison is performed with two conventional methods: one is PoseContrast, and the other is PosefromShape. In each comparison, we generate a side-by-side rendering image pair from the conventional and the proposed methods, as shown in Figure 9. For each RGB test image, the side-by-side rendering image pair is concatenated, and their poses are visually compared. Based on the comparison, we classify the results into four cases:  
1. Better: Rendering images from the proposed network yields a better pose appearance than the conventional method. An example is shown in Figures 9a and 10a. 2. SS (Same or Similar): Both CAD rendering images show the same or similar pose appearance. An example is shown in Figures 9b and 10b. 3. Worse: Rendering images from the conventional method is better than the proposed network. An example is shown in Figures 9c and 10c. 4. Bad: Both pose estimation results are significantly different from the pose of the RGB object. An example is shown in Figures 9d and 10d. Figure 9: Samples of CAD renderings and RGB images. From the left, renderings from PoseContrast, the proposed method, and the RGB object. The qualitative comparison is (**a**) Better, (**b**) SS, (**c**) Worse, and (**d**) Bad. We consider that the predicted rotational pose is significantly different from the RGB object if the pose difference is more than about 45\({}^{\circ}\). For the performance correlation analysis, the 'Bad' case is excluded. In Figure 9, the left image is the estimation result from PoseContrast, the center image is from the proposed method, and the right image is RGB objects. Tables 5 and 6 present the results of the qualitative evaluation. Each evaluation is denoted as 'Better', 'SS', and 'Worse', respectively. Table 5 shows the evaluation with respect to PoseContrast and Table 6 is to PosefromShape. The greater number of 'Better' evaluations is obtained in Table 6 because the performance enhancement compared to PosefromShape is much higher than that of PoseContrast. For comparative analysis with PosefromShape, we evaluate this method using the same dataset as our proposed method. Unlike PoseContrast, which predicts the pose from a single RGB image and renders the CAD model using the same predicted angle for all instances, PosefromShape predicts individual values for each pair of an RGB image and a CAD model used as input. Therefore, for our evaluation, we use the rendering of the ground truth CAD model to estimate the similarity measurement. Using the visual evaluation of the pose estimation, we performed an additional quantitative analysis by introducing a correlation score between the pose estimation and the CAD retrieval. In Table 4, we have shown the rank numbers of the matching CAD model using the two image similarity measurements. Let \(r_{i}\) be the rank number of the matching CAD model with the i-th test RGB images, \(r_{i,Proposed}\) the rank number results from the proposed method, and \(r_{i,Conventional}\) the rank number results from the conventional methods, PoseContrast or PosefromShape. We use Equation (32) to get the correlation score \(s_{i,corr}\) of the i-th test image.