# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 4 Experiments
### Main Results
** & **Hard** \\ \hline
3D-CVF at SPA [29] & RGB + Lidar & - & 89.20 & 80.05 & 73.11 & - & - & - & - & - \\ CLOCs [30] & RGB + Lidar & 10.0 & 88.94 & 80.67 & 77.15 & - & - & - & - & - \\ UberATC-MMF [31] & RGB + Lidar & 10.0 & 89.81 & 79.28 & 74.59 & - & - & - & - & - \\ EPNet++ [33] & RGB + Lidar & 10.0 & **91.37** & **81.96** & 76.71 & 76.15 & 59.71 & 53.67 & 52.79 & **44.38** & **41.29** \\ \hline PointRCNN [14] & Lidar only & 10.0 & 86.96 & 75.64 & 70.70 & 74.96 & 58.82 & 52.53 & 47.98 & 39.37 & 36.01 \\ Point-GNN [15] & Lidar only & - & 88.33 & 79.47 & 72.29 & 78.60 & 63.48 & 57.08 & 51.92 & 43.77 & 40.14 \\
3 DSSD [16] & Lidar only & 26.3 & 88.36 & 79.57 & 74.55 & **82.48** & 64.10 & 56.90 & **54.64** & 44.27 & 40.23 \\ SECOND [12] & Lidar only & 30.4 & 85.29 & 76.60 & 71.77 & 71.05 & 55.64 & 49.83 & 43.04 & 35.92 & 33.56 \\ PointPillars [9] & Lidar only & 42.4 & 82.58 & 74.31 & 68.99 & 77.10 & 58.65 & 51.92 & 51.45 & 41.92 & 38.89 \\ Part-A2-Net [34] & Lidar only & 12.5 & 87.81 & 78.49 & 73.51 & 79.17 & 63.52 & 56.93 & 53.10 & 43.35 & 40.06 \\ Voxel R-CNN [10] & Lidar only & 25.2 & 90.90 & 81.62 & **77.06** & - & - & - & - & - \\ Fast Point R-CNN [35] & Lidar only & - & 85.29 &RCNN, but 2.59%, 2.42% and 3.05% higher than Voxel R-CNN using the same second stage network. At the three levels of the car category, the performance of our method is similar to PV-RCNN, but lower than Voxel R-CNN with 1.44%, 0.73%, and 0.11%. Although our method has effectively improved the detection results of the cyclist category, combined with the comparison results in Tables 1-3, our method has not performed well in the detection task in the automobile category. There are several possible reasons for this. First, we project the voxel features to the X and Y directions, and then integrate the projection feature maps of the X and Y directions into the BEV feature map to supplement the geometric feature information missing from the BEV feature map due to dimension compression. Because the volume of cyclists and pedestrians in the three-dimensional space is small, and the projection area under the BEV perspective is also small, the detection network that only relies on the BEV perspective learns less features related to these two objects. In addition, in the KITTI dataset, the number of labels in the car category is three to four times that of cyclist and pedestrian. This imbalance makes it more difficult for the network to learn feature information related to cyclist and pedestrian detection tasks, making features related to car category dominant. Although our network has learned more features that are helpful for cyclist and pedestrian detection tasks, these features have certain side effects on car category detection tasks. Therefore, the detection performance of the cyclist and pedestrian has improved, while the detection performance of the car has slightly decreased. In addition, in Tables 1 and 2, the performance of our network in the pedestrian category is not very good. We believe there are two main reasons for this. First, the 3D spatial features extracted from our network are only voxel features. Due to the small volume of pedestrians in 3D space, it is difficult to extract effective features only with voxels, so the networks that perform well in pedestrian categories in Tables 1 and 2 are extracted original point features, or both voxels and original point features are extracted and fused. Second, the second stage network we selected is not suitable for the detection of pedestrians.The results in Table 3 show that at three levels of pedestrian category, the original Voxel R-CNN's \(AP_{3D}\) is 63.27%, 56.94%, and 51.56%, respectively. And our method is 2.59%, 2.42%, and 3.05% higher than the original Voxel R-CNN's \(AP_{3D}\).This means that our method has indeed learned more features of the pedestrian category, which proves our view.