# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Comparison of Algorithms Using the Occlusion LineMOD Dataset  
The experimental results on the Occlusion LineMOD dataset indicate the performance of various algorithms in pose estimation under occluded scenes. The challenge of this dataset lies in the partial occlusion of objects, requiring the algorithm to not only identify the target object but also accurately estimate its 6D pose in complex scenes. We used the AUC as the evaluation metric, which comprehensively reflects the robustness of the algorithm when handling occlusions.  
As shown in Table 5, the proposed method demonstrated outstanding performance in several object categories, showcasing strong competitiveness in occluded scenarios. For example, for objects with complex shapes and symmetric objects, such as the eggbox and glue, the proposed method achieved AUC scores of 63.9% and 77.3%, respectively, surpassing PVNet, HybridPose, and GDR-Net [51] and even approaching or exceeding the performance of CRT-6D. In these categories, the geometric properties and high occlusion rates make precise 6D pose estimation highly challenging, but the proposed method effectively addressed these issues, demonstrating its robustness in handling occlusions.  
Furthermore, for objects with severe occlusions, such as the duck and holepuncher, the proposed method outperformed most existing algorithms. For instance, in the duck category, our method achieved an AUC of 50.2%, surpassing both CRT-6D and GDR-Net, demonstrating its stronger capability to handle severe occlusion. Similarly, in the holepuncher category, the proposed method reached an AUC of 76.3%, outperforming GDR-Net and HybridPose and approaching the performance of CRT-6D.  
Figure 9: Visual comparison of pose estimation results. The different colors and shapes represent the point cloud visualizations of the same object predicted by different algorithms. Each color corresponds to the same object across different methods. The boxes around the objects highlight the areas of interest. The subfigures (**a**–**d**) show the predictions of the same image by different algorithms.  
Despite these promising results, we observed that pose estimation under severe occlusion remains challenging. Failures are mainly attributed to the lack of visible discriminative features when large portions of the object's surface are obscured. In such cases, the fused RGB-D features may be ambiguous or insufficient to guide accurate regression, especially for symmetric or textureless objects.  
Overall, the proposed method achieved an average AUC of 63.4% on the Occlusion LineMOD dataset, demonstrating strong competitiveness compared with the latest CRT-6D and SAM-6D methods. This indicates that the proposed approach offers both stability and accuracy in complex and occluded scenarios and still exhibits substantial potential under severe occlusion. By leveraging efficient computational strategies and multiscale geometric reasoning, the method achieves a favorable balance between estimation accuracy and resource consumption, making it well suited for practical applications in challenging environments.