## 4 Experiments
### Experiments on the SIAT Dataset  
**Semantic segmentation** Table 1 shows the segmentation accuracy of the proposed segmentation network and other state-of-the-art methods on the SIAT dataset. As shown in Table 1, the proposed segmentation network is much better than the other methods for both mIOU and mPA metrics, which proves the overall performance of our network. Figure 11 shows some qualitative results of our segmentation network and other methods, from which it can be observed that the results of our method are more accurate than those of other methods, especially at the edges of the objects. Furthermore, for the texture-less object in the first and second columns, it is difficult to obtain accurate and complete predictions using the previous state-of-the-art methods. Our segmentation results after fusing the depth information can better fit the ground truth.  
**Classification** The accuracy of our classification network reached 96.4%, which proves its robustness in dealing with similar objects, especially for objects with the same textures but different geometrical structures, such as cups from different perspectives, which cannot be distinguished from other segmented networks.  
**6D Pose estimation** Table 2 shows the accuracy comparison of our 6D pose estimation network and DenseFusion on the SIAT dataset, where Dense(per) represents the DenseFusion method without an iterative refinement network and Dense(iter) is the DenseFusion method using an iterative refinement network. Ours uses the same method as abbreviated. As shown in Table 2, our results without iterative refinement are close to the refined results of DenseFusion, and our refined result is significantly better than that of DenseFusion, which proves the accuracy of our method. Some quantitative results of DenseFusion and our pose-estimation network are shown in Figure 12. It can be observed that our method can obtain better results overall, especially for the untextured object in the fourth column of images. The estimation of DenseFusion has obvious errors, and our method can yield more accurate results.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **mIOU** & **mPA** \\ \hline U-Net [28] & 57.2 & 94.2 \\ DeepLabV3 [51] & 61.3 & 95.0 \\ PSPNet [29] & 65.2 & 95.6 \\ \hline Ours & 70.6 & 96.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of segmentation on SIAT Dataset.  
Figure 11: Some qualitative results of our segmentation network and other state-of-the-art segmentation networks. Different colors represent different categories.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dense(per)**} & \multicolumn{2}{c}{**Dense(iter)**} & \multicolumn{2}{c}{**Ours(per)**} & \multicolumn{2}{c}{**Ours(iter)**} \\ \cline{2-9}  & **AUC** & **ADD-S** & **AUC** & **ADD-S** & **AUC** & **ADD-S** & **AUC** & **ADD-S** \\ \hline toy & 97.4 & 88.6 & 97.4 & 88.6 & 88.8 & 97.2 & 88.8 & 97.8 \\ \hline Lay’s & 68.0 & 73.8 & 72.7 & 75.3 & 76.0 & 74.0 & 78.6 & 82.2 \\ \hline bowl & 97.4 & 91.5 & 97.4 & 91.5 & 92.2 & 97.7 & 92.3 & 99.1 \\ \hline Thermos cup & 50.0 & 58.9 & 50.0 & 58.9 & 73.6 & 61.6 & 73.6 & 61.6 \\ \hline Tea box & 69.2 & 82.0 & 69.2 & 82.0 & 75.2 & 68.8 & 79.5 & 68.8 \\ \hline Blue moon & 52.2 & 75.7 & 60.8 & 76.4 & 78.1 & 62.5 & 84.0 & 73.1 \\ \hline Metal block & 64.3 & 78.5 & 64.5 & 78.5 & 76.7 & 64.4 & 81.4 & 76.9 \\ \hline Carton & 71.7 & 83.4 & 71.7 & 83.4 & 75.1 & 66.1 & 80.1 & 75.3 \\ \hline cup & 96.3 & 85.9 & 97.6 & 87.6 & 87.9 & 97.7 & 88.9 & 99.5 \\ \hline back of cup & 92.7 & 88.2 & 92.7 & 88.2 & 87.7 & 94.0 & 90.1 & 98.1 \\ \hline MEAN & 75.7 & 79.5 & 77.3 & 81.0 & 81.4 & 78.7 & 83.7 & 83.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative evaluation of 6D pose on SIAT Dataset.