# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Comparison of Algorithms Using the YCB-Video Dataset  
For pose estimation on the YCB-Video dataset, we employed two evaluation metrics: the area under the ADD-S score curve (AUC) with a threshold from 0 to 10 cm, and the percentage of poses with an ADD-S error less than 2 cm, which reflects the high accuracy. In Table 4, the best-performing results for each object under both metrics are shown in bold. Objects marked with an asterisk (*) are symmetric and evaluated accordingly. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**G2L-Net [50]**} & \multicolumn{2}{c}{**FS6D [26]**} & \multicolumn{2}{c}{**SaMFNet [41]**} & \multicolumn{2}{c}{**DenseFusion [13]**} & \multicolumn{2}{c}{**MaskedFusion [35]**} & \multicolumn{2}{c}{**Ours**} \\ \hline  & **AUC** & **\textless{}2 cm** & **AUC** & **\textless{}2 cm** & **AUC** & **\textless{}2 cm** & **AUC** & **\textless{}2 cm** & **AUC** & **\textless{}2 cm** & **AUC** & **\textless{}2 cm** \\ \hline
002\_master\_chef\_can & 94.0 & - & 92.6 & - & 95.9 & **100.0** & 95.2 & **100.0** & 95.9 & **100.0** & **96.0** & **100.0** \\
003\_cracker\_box & 88.7 & - & 83.69 & - & 94.1 & 94.8 & 92.5 & 99.3 & **96.0** & **99.7** & 95.0 & 97.4 \\
004\_sugar\_box & 96.0 & - & 95.1 & - & 97.2 & **100.0** & 95.1 & **100.0** & 97.6 & **100.0** & **97.7** & **100.0** \\
005\_tomato\_soup\_can & 86.4 & - & 93.0 & - & **94.2** & **96.9** & 93.7 & **96.9** & **94.2** & 96.5 & **94.2** & 96.7 \\
006\_mustard\_bottle & 95.9 & - & 97.0 & - & 95.4 & **100.0** & 95.9 & **100.0** & 97.6 & **100.0** & **97.9** & **100.0** \\ c007\_tuna\_fish\_can & 96.0 & - & 94.5 & - & 94.7 & 99.0 & 94.9 & **100.0** & 96.7 & 99.9 & **97.0** & 99.7 \\
008\_pudding\_box & 93.5 & - & 94.9 & - & 95.9 & **100.0** & 94.7 & **100.0** & 96.3 & **100.0** & **96.6** & **100.0** \\
009\_gelatin\_box & 96.0 & - & **98.3** & - & 95.4 & **100.0** & 95.8 & **100.0** & 98.0 & **100.0** & 98.2 & **100.0** \\
010\_potted\_meat\_can & 93.5 & - & 87.6 & - & **94.7** & **100.0** & 90.1 & 93.1 & 89.4 & 90.8 & 91.3 & 93.6 \\
011\_banana & 96.8 & - & 94.0 & - & 97.3 & **100.0** & 91.5 & 93.9 & **97.5** & **100.0** & 97.0 & 98.9 \\
019\_pitcher\_base & 86.2 & - & 91.1 & - & 91.2 & 94.1 & 94.6 & **100.0** & 97.4 & 99.9 & **98.3** & **100.0** \\
021\_bleach\_cleanser & 91.8 & - & 89.4 & - & **96.7** & **100.0** & 94.3 & 99.8 & 93.8 & 94.0 & 95.2 & **100.0** \\
024\_bowl\^{*} & 84.3 & - & 74.7 & - & 83.8 & 84.1 & 86.6 & 69.5 & **90.1** & **95.8** & 89.6 & 91.4 \\
025\_mug & 92.0 & - & 86.5 & - & 95.5 & 99.6 & 95.5 & **100.0** & 97.0 & **100.0** & **97.6** & **100.0** \\
035\_power\_drill & 86.7 & - & 73.0 & - & 89.5 & 93.4 & 92.4 & 97.1 & 96.4 & **99.4** & **96.8** & **99.4** \\
036\_wood\_block\^{*} & 95.4 & - & 94.7 & - & **97.1** & **100.0** & 85.5 & 93.4 & 90.6 & 98.1 & 93.6 & 99.2 \\
037\_scissors & 92.3 & - & 74.2 & - & 84.1 & 67.4 & **96.4** & **100.0** & 93.2 & 99.0 & 89.3 & 92.3 \\
040\_large\_marker & 94.4 & - & **97.4** & - & 73.8 & 77.5 & 94.7 & 99.2 & 97.0 & **99.7** & 96.4 & 98.3 \\
051\_large\_clamp\^{*} & **92.3** & - & 82.7 & - & 67.6 & 66.1 & 71.6 & **78.5** & 72.1 & 77.9 & 73.0 & 76.8 \\
052\_extra\_large\_clamp\^{*} & 94.7 & - & 65.7 & - & **95.5** & **100.0** & 69.0 & 69.5 & 69.6 & 72.0 & 67.0 & 68.1 \\
061\_foam\_brick\^{*} & 92.6 & - & 95.7 & - & 92.6 & 95.6 & 92.4 & **100.0** & 94.2 & **100.0** & **96.6** & **100.0** \\ \hline
**MEAN** & 92.3 & - & 88.4 & - & 92.6 & 95.6 & 91.2 & 95.3 & 92.9 & **96.3** & **93.1** & 95.9 \\ \hline \hline \end{tabular}  
* **Notes:** Bold numbers represent the highest scores among the methods, and objects marked with an asterisk (*) are symmetric.  
\end{table} 
Table 4: Evaluation results of different algorithms on the YCB-Video dataset. Figure 8: Comparison of pose estimation effectiveness for weakly textured objects.The red dots represent the point cloud result obtained by transforming the predicted poses of the object.