# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud
## 4 Experiments and Discussions
### Ablation Study and Qualitative Results
\begin{table}
\begin{tabular}{c c c} \hline  & **Methods** & **mIoU** \\ \hline _a._ & VoteNet & 58.6 \\ _b._ & VoteNet + \(F\) & 63.2 \\ _c._ & VoteNet + \(F\) + \(V\) & 69.1 \\ _d._ & VoteNet + \(W\) + \(C\) & 68.3 \\ _e._ & VoteNet + \(F\) + \(V\) + \(C\) & 70.7 \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation study of the proposed REGNet model on ScanNet V2 dataset; \(F\), \(V\), and \(C\) denote foreground-aware module, voting-aware module, and cluster-aware module, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Backbone** & **Model Size** & **[EMAIL_ADDRESS]** \\ \hline VoteNet [32] & PointNet++ & 11.2 M & 58.6 \\ GFNet (L6, O256) [31] & PointNet++ & 14.5 M & 67.3 \\ GFNet (L12, O512) [31] & PointNet++w2\(\times\) & 29.6 M & 69.1 \\ MLCVNet [29] & PointNet++ & 13.9 M & 64.5 \\ H3 DNet [30] & 4\(\times\)PointNet++ & 56.0 M & 67.2 \\ MCGNet [41] & 4\(\times\)PointNet++ & 57.2 M & 67.5 \\ \hline REGNet & PointNet++w2\(\times\) & 37.6 M & 70.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with VoteNet [31], GFNet, MLCVNet, H3 DNet, and MCGNet with various configurations on ScanNet V2. The main comparison is based on the best results of multiple experiments between different methods, and the number within the bracket is the average result. Here, 4\(\times\)PointNet++ denotes that this method adopted four-branch individual PointNet++ as backbone; PointNet++w2\(\times\) denotes that the backbone width in the method is expanded by 2; \(L\) denotes the depth of decoder, and \(O\) denotes the number of the proposal. Figure 5: Qualitative comparison results between the use and non-use of the proposed foreground-aware module on VoteNet: (**a**) visualization results of VoteNet, (**b**) visualization results of VoteNet with our foreground-aware module, and (**c**) corresponding ground truth. The bounding boxes in VoteNet are more cluttered, with some being misclassified, whereas our foreground-aware module produces more concise and reasonable results. Best viewed in color. Figure 6: Qualitative comparison results with and without the proposed voting-aware module on VoteNet: (**a**) visualization results of VoteNet, (**b**) visualization results of VoteNet with our voting-aware module, and (**c**) corresponding ground truth. The red and green dots represent the voting centers. In (**a**), VoteNet does not consider the relationship between the voting centers, which leads to some misclassifications, such as the object in the upper right corner being classified as a table, but there is nothing but cluttered points. Using the proposed voting-aware module, more reliable results can be achieved, as shown in (**b**). Best viewed in color. Figure 8: Performance analysis. Per-class [EMAIL_ADDRESS] improvement of our REGNet over VoteNet, VENet, and GFNet on the ScanNet V2 dataset. Best viewed in color. Figure 7: Qualitative comparison results between using and not using the cluster-aware module on VoteNet: (**a**) original point cloud of the scene, (**b**) bounding box results of VoteNet, (**c**) visualization results of VoteNet combined with our cluster-aware module, and (**d**) corresponding ground truth. The red dashed circles give detailed improvement visualization results. Best viewed in color.