# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 3 Results
### Test Results  
Testing experiments were conducted on the LINEMOD dataset, the Occlusion LINEMOD dataset and the Truncation LINEMOD dataset, using the ADD metric to evaluate accuracy (in %). Note that glue and eggbox are considered as symmetric objects, so their experimental results were evaluated by ADD-S. The test results on LINEMOD is shown in Table 1. In \"two-stage\" part, the first column represents RPVNet and the second shows the results of the proposed method using heatmap instead of direction map. The remaining columns show the results of other studies, including end-to-end methods (Deep6D and SSD-6D) and two-stage methods (Tekin, BB8, Cull and PVNet). The test results on Occlusion LINEMOD is shown in Table 2. In the table, PoseCNN is the only end-to-end method and others are two-stage method. The comparison between predicted and ground truth pose for RPVNet is shown in Figure 7: the 3D bounding box is projected onto the image according to the pose information. The green box represents predicted value, and the blue one represents ground truth. The above two tables also show the average accuracy (Average) and the standard deviation (Std-Dev) for each method. In addition to this, the last row of each table, Wilcoxon\({}_{t}\), shows the results (Yes/No) of the two-sided Wilcoxon rank sum test [39] at 0.05 significance level for the hypothesis \"there is a significant difference between RPVNet and the method represented by the current column\". \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Two-Stage**} & \multicolumn{4}{c}{**End-to-End**} \\ \hline  & **RPVNet** & **Heatmap** & **Tekin [16]** & **BB8 [19]** & **Cull [17]** & **PVNet [29]** & **Deep6D [10]** & **SSD-6D [12]** \\ \hline Ape & **55.6** & 42.0 & 21.6 & 40.4 & 55.1 & 43.6 & 38.8 & - \\ Benchvise & 98.7 & 92.7 & 81.8 & 91.8 & 89.0 & **99.9** & 71.2 & - \\ Cam & 83.6 & 82.2 & 36.6 & 55.7 & 66.2 & **86.8** & 52.5 & - \\ Can & 93.2 & 91.3 & 68.8 & 64.1 & 89.2 & **95.5** & 86.1 & - \\ Cat & 75.5 & 73.1 & 41.8 & 62.6 & 75.3 & **79.3** & 66.2 & - \\ Driller & 94.7 & 91.0 & 63.5 & 74.4 & 88.6 & **96.4** & 82.3 & - \\ Duck & **63.5** & 51.4 & 27.2 & 44.3 & 41.8 & 52.6 & 32.5 & - \\ Eggbox & 95.8 & 94.6 & 69.6 & 57.8 & 97.1 & **99.2** & 79.4 & - \\ Glue & 93.4 & 89.8 & 80.0 & 41.2 & 94.6 & **95.7** & 63.7 & - \\ Hole & **82.5** & 79.5 & 42.6 & 67.2 & 68.9 & 81.9 & 56.4 & - \\ Iron & 96.1 & 91.9 & 74.9 & 84.7 & 90.9 & **98.9** & 65.1 & - \\ Lamp & 96.8 & 94.7 & 71.1 & 76.5 & 94.2 & **99.3** & 89.4 & - \\ Phone & 91.5 & 88.5 & 47.7 & 54.0 & 67.6 & **92.4** & 65.0 & - \\ \hline Average & 86.1 & 81.7 & 55.9 & 62.7 & 78.3 & **86.4** & 65.2 & 76.3 \\ Std-Dev & **15.6** & 18.9 & 20.5 & 16.1 & 17.3 & 18.3 & 17.2 & - \\ \hline Wilcoxon\({}_{t}\) & - & Yes & Yes & Yes & Yes & No & Yes & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test results of accuracy on LINEMOD. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Two-Stage**} & \multicolumn{4}{c}{**End-to-End**} \\ \hline  & **RPVNet** & **Heatmap** & **Tekin** & **Oberweger [25]** & **PVNet** & **PoseCNN [11]** \\ \hline Ape & **17.9** & 9.8 & 2.48 & 17.6 & 15.8 & 9.6 \\ Can & **69.5** & 49.3 & 17.5 & 53.9 & 63.3 & 45.2 \\ Cat & **19.0** & 11.6 & 0.7 & 3.3 & 16.7 & 0.93 \\ Duck & **31.1** & 20.7 & 1.1 & 19.2 & 25.2 & 19.6 \\ Driller & 63.7 & 48.1 & 7.7 & 62.4 & **65.6** & 41.4 \\ Eggbox & **59.2** & 30.3 & - & 25.9 & 50.2 & 22.0 \\ Glue & 46.6 & 38.5 & 10.1 & 39.6 & **49.6** & 38.5 \\ Holepuncher & **42.8** & 28.7 & 5.45 & 21.3 & 39.6 & 22.1 \\ \hline Average & **43.7** & 29.6 & 6.4 & 30.4 & 40.8 & 24.9 \\ Std-Dev & 19.8 & 15.1 & **5.99** & 19.9 & 19.7 & 15.7 \\ \hline Wilcoxon\({}_{t}\) & - & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results of accuracy on Occlusion LINEMOD.