# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud
## 4 Experiments and Discussions
### Ablation Study and Qualitative Results  
In this section, a set of ablative studies are conducted on the ScanNet V2 and SUN RGB-D datasets to demonstrate the effectiveness of the three proposed modules of our REGNet. **Effectiveness of the foreground-aware module. ** To demonstrate the effectiveness of the foreground-aware module, we provide its performance across different architecture methods, as shown in Table 3. In the experiments, we replace the standard backbone (PointNet++) in three state-of-the-art architectures: VoteNet, H3 DNet, and GFNet (L6, O256). Due to the fact that VoteNet and GFNet both use a branch of PointNet++ to extract feature representations, we only replace the backbone part. For H3 DNet, there are four branches in the backbone used to extract four different geometric primitives, and we replaces all four branches. To better validate the effectiveness of the module, we visualize the results of VoteNet with and without the module, as shown in Figure 5. From the visual comparison of (a) and (b), it can be observed that without the module, the localization of object bounding boxes is prone to error, and misclassification can occur. With the module, both localization and classification have been improved accordingly. **Effectiveness of the voting-aware module. ** To validate the advantages of the voting-aware module, we provide visual results with and without using the module on the validation dataset of ScanNet V2, as shown in Figure 6. In Figure 6a, we visualize some voting centers, shown as green points around the table and red points on the table. In VoteNet, the relationship between voting centers is not considered, resulting in many incorrect bounding boxes. However, when the voting perception module is used in VoteNet, the boundary boxes of the table are more concise, and the predicted results are more reasonable. **Effectiveness of the cluster-aware module. ** Finally, to demonstrate the effectiveness of our cluster-aware module, we suggest conducting experiments on ScanNet V2 with and without the module. The qualitative results are shown in Figure 7. From left to right, we provide the raw point cloud, the visualization results of VoteNet, the results of VoteNet with \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Methods** & **Presented at** & **Table** & **Sofa** & **Bookshelf** & **Chair** & **Desk** & **Dresser** & **Nightstand** & **Bed** & **Bathtub** & **Tolilet** & **[EMAIL_ADDRESS]** \\ \hline F-PointNet [14] & CVPR’18 & 51.1 & 61.1 & 33.3 & 64.2 & 24.7 & 32.0 & 58.1 & 81.1 & 43.3 & 90.9 & 54.0 \\ VoteNet [32] & ICCV’19 & 47.3 & 64.0 & 28.8 & 75.3 & 22.0 & 29.8 & 62.2 & 83.0 & 74.4 & 90.1 & 57.7 \\ CRNet [52] & ISPRS20 & 51.1 & 64.8 & 29.3 & 76.2 & 26.0 & 26.1 & 59.2 & 84.3 & 76.8 & 90.4 & 58.4 \\ MLCVNet [29] & CVPR’20 & 50.4 & 66.3 & 31.9 & 75.8 & 26.5 & 31.3 & 61.5 & 85.8 & 79.2 & 89.1 & 59.8 \\ InVoteNet [60] & CVPR’20 & 51.1 & 70.7 & 41.3 & 76.7 & 28.7 & 41.4 & 69.9 & 87.6 & 75.9 & 90.5 & 63.4 \\ H3 DNet [30] & ECCV’20 & 50.8 & 66.5 & 31.0 & 76.7 & 29.6 & 33.4 & 65.5 & 85.6 & 73.8 & 88.2 & 60.1 \\ GFNet [31] & ICCV’21 & 53.8 & 70.0 & 32.5 & 79.4 & 32.6 & 36.0 & 66.7 & 87.8 & 80.0 & 91.1 & 63.0 (62.6) \\ HyperoD59 [59] & CVPR’22 & – & – & – & – & – & – & – & – & – & – & – & 63.5 \\ RGNet [34] & CVPR’22 & – & – & – & – & – & – & – & – & – & – & 64.1 (63.6) \\ \hline Our GFNet & – & 54.0 & 69.1 & 34.1 & 79.7 & 33.9 & 41.0 & 70.8 & 88.1 & 79.2 & 91.7 & 64.1 (63.7) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison on the SUN RGB-D validation set with state-of-the-art methods. The top two results are shown in red and blue, respectively. Figure 3: The qualitative comparison results of 3D object detection on ScanNet V2 are presented. We provide a comparison between VoteNet and GFNet, as well as the raw point clouds and corresponding ground truth for comparison. The red dashed bounding boxes provide detailed information on qualitative improvements.