# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 4 Experiments
### Performance Analysis  
**Failure Cases.** To explore potential shortcomings, we further conducted individual assessment tests for each object, and the results are presented in Table 4. While we observed that our framework achieved high scores on the majority of objects, the lower scores on a few objects did impact the overall performance. Upon analysis of the corresponding results and sample data, the primary reason for these lower scores can be attributed to the excessively cluttered scenes encountered in the test samples. Taking object 23 as an example, as depicted in Figure 8, the object was heavily occluded by other objects in the scene, which interfered with the assessment of color and depth. In some test cases, the structure of the object was almost entirely occluded, making the estimation of the object's pose very challenging. Additionally, we observed that when key structures of the object were obscured, as shown in Figure 9, the model struggled to accurately determine its precise pose.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{4}{c}{**Mean VSD Recall**} \\ \cline{2-4}  & **Seen Obj. 1–18** & **Unseen Obj. 19–30** & **Total** \\ \hline DCPSPose-basic-1 CH & 63.05 & 61.62 & 62.34 \\ DCPSose-basic & 65.86 & 62.53 & 64.20 \\ DCPSPose-masked-1 CH & 65.54 & 63.89 & 67.22 \\ DCPSose-masked & 69.74 & 68.48 & 69.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results. The “basic” is the foundational version of our framework. The “masked” corresponds to the experiment of Section 4.3. The “1 CH” corresponds to the experiment of Section 4.4.  
**Time consumption.** The time consumption of our method in template rendering, feature extraction, and online inference is presented in Table 5. The query and template images used here, including both RGB and depth maps, have the same resolution of \(224\times 224\). The data presented here were obtained through actual measurements on the hardware platform mentioned in Section 4.1. Thanks to the separation of offline and online processes, our framework achieves real-time processing capabilities suitable for practical applications.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline
**Obj.** & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** & **\#6** & **\#7** & **\#8** & **\#9** & **\#10** & **\#11** & **\#12** & **\#13** & **\#14** & **\#15** & **\#16** & **\#17** & **\#18** \\ \hline
**VSD** & 0.65 & 0.70 & 0.85 & 0.65 & 0.68 & 0.77 & 0.43 & 0.27 & 0.52 & 0.64 & 0.72 & 0.68 & 0.61 & 0.71 & 0.74 & 0.87 & 0.83 & 0.76 \\ \hline
**Obj.** & **\#19** & **\#20** & **\#21** & **\#22** & **\#23** & **\#24** & **\#25** & **\#26** & **\#27** & **\#28** & **\#29** & **\#30** & & & & & & \\ \hline
**VSD** & 0.70 & 0.58 & 0.50 & 0.70 & 0.48 & 0.60 & 0.62 & 0.73 & 0.44 & 0.69 & 0.65 & 0.84 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Separate evaluation for each individual object in the T-LESS dataset. While our method achieved promising results for the majority of objects, there were a few specific objects where the scores were comparatively lower.  
Figure 8: Visualization of failure cases of object #23. In some test cases, severe occlusion of objects not only disrupts color information but also renders depth information ineffective in providing meaningful support.  
Figure 9: Visualization of failure cases. The occlusion of key structures results in the inability to accurately estimate the pose.