# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 4 Discussion
### Comparison with Other Methods  
**Comparison with end-to-end methods.** On LINEMOD, SSD-6D [12] with additional refinement and Deep6D [10] have average accuracies of 76.3% and 65.2%, respectively. On Occlusion LINEMOD, the accuracy of PoseCNN [11] is 24.9%. Although the standard deviation of RPVNet is not the best, the accuracy of RPVNet outperforms on both dataset (86.1%,43.7%,respectively) and the Wilcoxon tests verify this. Basically, end-to-end methods try to predict the pose of target objects directly from the features extracted from image, such as Deep6D and PoseCNN. Some might get more accurate result with refinement, such as SSD-6D. The pose of an object is a state space with six degrees of freedom. Its high dimensionality makes it difficult to directly predict pose from image, even if the range of values is restricted. In contrast, the object's keypoints have only two degrees of  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & Ape & Benchivise & Cam & Can & Cat & Driller & Duck & Eggbox \\ \hline
**PVNet** & 12.8 & **42.8** & **27.7** & **32.9** & **25.2** & 37.0 & 12.4 & **44.1** \\
**RPVNet** & **14.1** & 41.7 & 27.3 & 32.6 & 24.9 & **37.2** & **14.8** & 43.5 \\ \hline  & Glue & Hole & Iron & lamp & Phone & Average & Std-Dev & Wilcoxon\({}_{t}\) \\ \hline
**PVNet** & 38.1 & 22.4 & **42.0** & **40.9** & **30.9** & **31.5** & **9.5** & No \\
**RPVNet** & **38.4** & **23.2** & 40.9 & 38.8 & 30.0 & 31.3 & 10.4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of accuracy on Truncation LINEMOD.  
Figure 7: Comparison between predicted and ground truth pose.  
freedom and are restricted to a specific area in the image, making the prediction easier and more accurate.  
**Comparison with two-stage methods**. On LINEMOD, the average accuracy of RPVNet (86.1%) exceeds that of other two-stage methods except PVNet, such as Tekin (55.9%), BB8 (62.7%) and CullNet (78.3%). RPVNet (43.7%) is also more accurate than Tekin (6.4%) on Occlusion LINEMOD. The results of the Wilcoxon test verify that RPVNet is more accurate than these methods. If we compare PVNet with these methods, similar results can be drawn. This shows that estimating the keypoints on an object by predicting the direction map is more accurate than directly predicting the keypoints or the vertices of its bounding box.  
PVNet is currently the state of the art. Although the average accuracy of RPVNet on LINEMOD (86.1%) is not higher than that of PVNet (86.4%), according to Table 1, RPVNet is considered close to PVNet, since the Wilcoxon test verifies that there is no significant difference between them. On Occlusion LINEMOD, RPVNet (43.7%) outperforms PVNet (40.8%), and the Wilcoxon test verifies this, which shows that there is significant difference between the two methods. RPVNet also performs better than PVNet When estimating small size objects in occluded scenes, such as 'ape', 'cat' and 'duck'. RPVNet uses the RoI specified by object's bounding box to extract local features for prediction, while PVNet uses the whole features produced by backbone for prediction. These test results show that, in occluded scenes, using only partial features can improve robustness, as well as the accuracy of prediction for small-sized objects. Oberweger [25] also uses part of the features, but the performance (30.8%) is limited due to the drawback of heatmap and the random selection of local features.  
**Comparison on Truncation LINEMOD**. According to Table 3, the average accuracy of RPVNet (31.3%) is close to that of PVNet (31.5%), which is supported by the Wilcoxon test. On the one hand, the images in the Truncation LINEMOD are relatively small, and the difference between whole features and local features is relatively small. On the other hand, the use of local features is also ineffective in reducing the interference of irrelevant pixels, when the pixels of the target object are extremely scarce.