# A Texture-Based Simulation Framework for Pose Estimation
## 5 Discussion and Conclusions  
The development of robust 3D pose estimation systems for symmetric objects, such as spheres, presents a crucial advancement in industrial automation, robotic manipulation, precision metrology [31; 32], and the biomedical field [33]. Existing methods have explored the use of printed texture patterns on spheres for attitude determination, yet these techniques often struggle with limited generalization due to texture sensitivity and the trade-off between pattern complexity and computational demands. This work builds upon these existing efforts by addressing the limitations of traditional approaches through a texture-optimized design tailored for a synthetic dataset. Our results demonstrate that a high-complexity texture design, incorporating both multi-scale directional patterns and high chromatic contrast, leads to significant performance improvements. This enables substantial reductions in the MAE compared to low-complexity textures, thus directly tackling the dual bottlenecks of data scarcity and rotational ambiguity inherent in spherical object localization. This enhancement aligns with [PERSON]'s texture theory, where multi  
Figure 6: Box plot of test image error. scale directional features and chromatic contrast improve feature discriminability, enabling robust pose prediction under varying viewpoints. Theoretically, this work establishes a novel paradigm for texture-driven synthetic data generation. The observed correlation between texture complexity and model accuracy highlights the importance of the feature information provided by the texture feature design. The dual role of color--enhancing the validation accuracy while introducing noise in low-complexity scenarios--provides new insights into color-texture interactions. Here are the three key findings from this study:  
* Texture complexity dominance: high-complexity color textures (Texture_3_color) achieved the optimal accuracy, reducing errors by 64.8% compared to low-complexity designs. * Color-texture synergy: color enhanced performance in complex textures (with the test MAE achieving 0.731\({}^{\circ}\) and RMSE achieving 0.876\({}^{\circ}\)) but degraded the low-complexity results, emphasizing complexity as a prerequisite for effective color utilization. * Real-world generalization: the physical tests confirmed the feasibility, with the average attitude error measured by the real system reaching around 3\({}^{\circ}\) and 75% of the test data errors being less than 4\({}^{\circ}\), which ensures the feasibility of training the network with 2D data for 3D attitude estimation. These results provide a foundation for texture-driven synthetic data systems with applications in industrial detection and the related applications of target attitude estimation and motion analysis. This study is subject to two key limitations. First, the simulation framework assumes ideal material-light interactions, which may not fully capture real-world scenarios with reflective or translucent surfaces. Second, the rotational symmetries in spherical objects introduce inherent ambiguity in pose estimation. Due to the rotational symmetry of a sphere, an insufficient texture design can result in the object's appearance after rotation being indistinguishable from its original state, negatively impacting the measurement accuracy. This is particularly pronounced under extreme lighting variations, where cameras struggle to capture subtle differences in surface textures (such as color gradients or tiny marks). This difficulty further weakens the system's ability to differentiate between various rotation angles. Future work should integrate dynamic lighting models, like ray tracing and material-aware texture mapping, for more realistic simulations. To overcome rotational ambiguity, research should focus on designing more distinctive textures with invariant features. Furthermore, exploring sensor fusion with IMUs and incorporating prior knowledge of object motion could enhance pose estimation robustness.