# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 5 Conclusions  
In this work, we propose a multi-view joint learning and BEV feature-fusion network that combines BEV features extracted from different point cloud representations. The backbone of the feature extraction network consists of two branches. The pseudo-image-based feature-extraction branch encodes the point cloud and projects it onto the BEV perspective to directly extract BEV features. The voxel-based feature-extraction branch discretizes the point cloud into voxels. After spatial sparse convolution, the feature is projected onto the RV, SV, and the BEV. The features of the three perspectives are fused in the MVFF module to obtain BEV features. Then, the network adaptively fuses the BEV features of the two branch outputs through the M-BEV FF module to generate a 3D ROI, which is refined in the second stage of the network. According to the experimental results of the test set and the val set of the KITTI dataset, our network has successfully improved the detection results for the cyclist and pedestrian categories, which shows the effectiveness of our proposed network structure and the new modules. Although our network performs well in bicycle and pedestrian detection tasks, there are also certain limitations. For example, the network improves the detection performance of bicycle and pedestrian categories while reducing the detection performance of vehicle categories. Therefore, it is worth considering using a better fusion strategy to fuse features from multiple views, including RV, SV, BEV, and perform joint learning to obtain fusion features that contain more information, achieving better 3D object-detection results.In addition, because our network has two feature-extraction branches, it consumes a large amount of computing resources and time resources in feature extraction. Therefore, we can also consider improving the network in the future to ensure that the detection accuracy is equivalent to or slightly lower than the existing detection accuracy, while reducing complexity or computing costs, so that the network has better practical performance. Conceptualization, methodology, software, data curation, writing--original draft, Q.L. ; investigation, formal analysis, X.L. ; investigation, data curation, X.Z. ; funding acquisition, supervision, writing--review and editing, X.T. ; data curation, B.S. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**PI-Based**} & \multirow{2}{*}{**V-Based**} & \multirow{2}{*}{**MV**} & \multirow{2}{*}{**M-BEV**} & \multicolumn{3}{c}{**Car—\(AP_{3D}\)(\%)**} & \multicolumn{3}{c}{**Cyclist—\(AP_{3D}\)(\%)**} & \multicolumn{3}{c}{**Pedestrian—\(AP_{3D}\)(\%)**} \\  & & & & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline (a) & ✓ & & & & 87.89 & 78.28 & 75.46 & 83.07 & 64.42 & 60.09 & 53.29 & 47.77 & 43.88 \\ (b) & & ✓ & & & 93.09 & 85.04 & 82.31 & 92.10 & 73.28 & 70.13 & 63.27 & 56.94 & 51.56 \\ (c) & ✓ & ✓ & & ✓ & 91.57 & 82.64 & 81.86 & 92.47 & 73.29 & 68.71 & 65.50 & 58.97 & 54.59 \\ (d) & ✓ & ✓ & ✓ & ✓ & 91.65 & 84.31 & 82.20 & 93.58 & 74.57 & 71.10 & 65.86 & 59.39 & 54.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of the two modules that we propose on the performance of the KITTI val set. PI-based is pseudo-image based feature extraction. V-based is Voxel-based feature extraction. “MV” and “M-BEV” represent the multi-view feature fusion module and multi-BEV feature fusion module, respectively. The results are evaluated with the average precision calculated by 40 recall positions for the cyclist class. **Funding:** This work was supported by the Key-Area Research and Development Program of Guangdong Province under Grant (2020B0909030005 and 2020B090921003). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.