# A Texture-Based Simulation Framework for Pose Estimation
## 4 Experiments  
To verify the accuracy of the texture design, texture is attached to the simulated particle model for modeling, and the physical spherical particles are 3D-printed. At the same time, a real machine vision system is built using an industrial CMOS camera, a triaxial angular displacement table, and a personal computer. Figure 5 shows a photo of the entire system setup.  
In the experiments, the pose of the spherical particle is changed using an angular displacement table. A camera is used to collect the 2D projection image corresponding to the 3D pose, which is then transmitted to a computer. The obtained images are processed to be applicable to neural network algorithms. In this experiment, the neural network used is still MobileNet trained to work with texture.  
The above system is used to collect 40 real images with different attitude angles, and, after processing the images, the MobileNet network is used to estimate the actual attitude. The error between the estimated result and the actual attitude is analyzed statistically. The detailed analysis is shown in Table 3 below, and a more specific visualization is shown in Figure 6 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Mean Error** & **Std** & **RMSE** & **Maximum** \\ \hline X-axis & 2.717 & 2.34 & 3.585 & 11.843 \\ Y-axis & 3.275 & 3.718 & 4.955 & 15.273 \\ Z-axis & 3.223 & 2.031 & 3.810 & 8.511 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error analysis of test data.  
Figure 5: The machine vision system.  
It can be seen from Table 3 that the model trained with the virtual dataset still has a low MAE (2.7-3.3\({}^{\circ}\)) in the practical application, which means good practical application prospects. In the field of attitude estimation for symmetric objects such as spheres, [PERSON] obtained a matching error of about 2\({}^{\circ}\) and a weighted error of 3\({}^{\circ}\) by matching synthetic textures with stereo vision. [PERSON]'s method, based on optimized surface patterns, achieved an MAE of about 4\({}^{\circ}\) in a controlled environment with SNR = 2. [PERSON] extended the matching algorithm to 6-DoF pose detection of complex parts by constructing a multi-view template library offline based on CAD models, achieving position errors < 2 mm and pose errors \(\approx\) 3\({}^{\circ}\)[30]. In contrast, the texture optimization framework proposed in this paper achieves a lower average error (2.7-3.3\({}^{\circ}\)) in real scenes, indicating its effectiveness. However, the deviation along the \(y\)-axis is somewhat large, with a Std of 3.718\({}^{\circ}\), an RMSE of 4.955\({}^{\circ}\), and a maximum prediction error of 15\({}^{\circ}\), indicating that the model's predictions in this direction are less stable. The test error box diagram in Figure 6 further reflects the error distribution of each coordinate axis. As can be seen from the figure, the median error of each axis is relatively close, distributed around 2-3\({}^{\circ}\), and the upper edge of the box is almost within 4\({}^{\circ}\), indicating that 75% of the data prediction is more accurate, indicating that this method is feasible. However, there are several outliers on the \(x\)-axis and \(y\)-axis, indicating that the prediction performance of the current model is not very stable and there will be deviations. Further research is needed to understand and mitigate the anomalies for more robust real-world performance.