## 3 Results and Discussion  
In this research, we addressed the problem of camera 3D localization for tasks involving the reconstruction of a 3D model of the ear. We proposed a simple solution to improve the accuracy of 3D camera localization based on enriching the background texture--specifically, using a hat with a particular pattern. To ascertain the effectiveness of this 'hat' solution and compare how different patterns might enhance camera localization accuracy, we generated a specific dataset to test these hypotheses. We performed a comparative evaluation of the influence of different cap textures on the precision of 3D camera localization and presented the comparative results of solutions to improve camera 3D localization in Figure 4.  
The results of the experimental comparison presented in Figure 4 comprise a comparison of 13 experimental scenarios (cases). The first column (block of data points) in the chart presents the results of the baseline case when no cap was used. The subsequent columns display the results of scenarios when different caps were used. The experimental scenarios are depicted in Figure 2 and summarized in Section 2.2. The camera 3D localization accuracy was evaluated in different experimental cases (the evaluation method is described in Section 2.3), and the errors are graphed in Figure 4.  
The data for evaluation were collected in four different experimental setups (as described in Section 2.5); therefore, the results are grouped in Figure 4, and the color along with the marker type denotes the particular experimental setup. Each data point corresponds to a separate experiment. The data points are arranged in four virtual columns, and the fifth column shows the summary statistics using a box plot that incorporates all the data points from the first four columns. The main conclusion drawn from the results presented in Figure 4 is that the use of a hat reduces camera localization errors--errors in the first data block (\"no cap\" experimental case) are the highest. We observed that some reconstruction experiments were unsuccessful (the photogrammetry pipeline failed to reconstruct the sparse scene). These failures are summarized and presented as bar charts beneath the horizontal axis. The height of the bar indicates the percentage of failed reconstructions. The \"dots 4\" (regularly arranged dot pattern of the fourth size dots), \"dots 3\", \"checkboard\", \"no cap\", and \"dots 1\" cases included failed reconstructions. Figure 4: Results of experimental comparison of proposed camera 3D localization improvement solutions. In the graph, the results of 13 experimental cases are summarized. Each case consists of data arranged in 5 virtual columns: the first four columns show the separate experiment accuracy evaluation results (camera localization errors) of the four experimental setups, and the fifth column shows summary statistics with a box plot, which includes all data points from the first four columns. As some reconstruction experiments were not successful (photogrammetry pipeline failed to reconstruct the sparse scene), these failures are summarized and presented as bar charts under the horizontal axis. Full-sized bars mean that 100% of cases failed. The same color of the bar charts and data points labels the same experimental setup. The lowest reconstruction errors and all successful camera 3D location reconstructions were achieved using a regular knitted patterned winter hat (\"winter hat\" case), caps with sparse dot patterns (\"dots 1s\", \"dots 4s\", \"dots 3s\", \"dots 2s\"), and a hat with a pattern of randomly painted blobs of different sizes (\"painted blobs\"). All the hats with sparse-dotted patterns performed well regardless of the pattern's dot size. Caps with a printed dot pattern offer the greatest potential to aid in the reconstruction of the scale of the 3D ear model. However, the weakness of the \"painted blobs\" hat is that it was painted manually.