# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Comparison of Algorithms Using the YCB-Video Dataset
Considering the AUC metric, our proposed method achieves an average accuracy of 93.1%, outperforming G2L-Net (92.3%), FS6D (88.4%), SaMFNet (92.6%), DenseFusion (91.2%), and MaskedFusion (92.9%). Compared to DenseFusion and FS6D, our method shows improvements of 1.9% and 4.7%, respectively, demonstrating stronger capability in pose regression. Considering the <2 cm accuracy metric, our method achieves 95.9%, slightly outperforming SaMFNet (95.6%) and DenseFusion (95.3%) and remaining competitive with MaskedFusion (96.3%). Notably, our method achieves 100% accuracy on several challenging objects such as mug, gelatin_box, and mustard_bottle, showcasing its effectiveness in precise pose estimation, particularly for symmetric or small-scale objects. Although FS6D demonstrates good generalization in few-shot scenarios, its overall AUC accuracy remains lower than our method, likely due to the absence of depth inputs. Overall, our method consistently ranks among the top-performing methods across both metrics and object categories, showing strong generalization and robustness. However, as shown in Table 4, all methods, including ours, struggled with the large_clamp and extra_large_clamp objects, resulting in a relatively low accuracy. This is mainly due to the use of PoseCNN for semantic segmentation, as the two clamps are nearly identical in appearance except for scale, making it difficult to produce accurate masks and thus impacting pose estimation. Despite these challenges, our approach achieves notable improvements in overall pose estimation accuracy, particularly considering the <2 cm metric, and demonstrates enhanced robustness and precision compared to several representative RGB-D methods. To provide a more intuitive comparison of the prediction results from different algorithms on the YCB-Video dataset, we visualized the estimation results of each method. All methods used the semantic segmentation output from the PoseCNN network. We combined the sampled point information to transform the predicted results into point clouds and projected them onto the original image. This allowed for a direct observation of the consistency between the estimated pose and the ground truth. The comparison results are shown in Figure 9. As seen in Figure 9, for smooth and textureless objects such as bananas and bleach cleaners, the method proposed in this paper provides accurate pose estimation, and for objects that lack texture features and have complex shapes, the pose estimates from our method were closer to the ground truth (as shown in Figure 9). This improvement was attributed to the optimization of depth information processing in our method, which effectively integrated image and depth data to leverage richer features for more precise pose estimation. Moreover, the effective fusion of texture and depth features ensured that our method maintained good compatibility with symmetric objects, such as bowls and wooden blocks (as shown in Figure 9,c). Furthermore, by using pixel-level prediction and enhancing the multi-scale local attention of point clouds, our method demonstrated strong robustness to occlusion and was able to provide accurate predictions for severely occluded objects, such as the scissors in Figure 9.