# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 4 Experiments
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Modality**} & \multirow{2}{*}{\begin{tabular}{c} **FPS** \\ **(Hz)** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Car—\(AP_{\text{AD}}\)(\%)** \\ **Easy** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Cyclist—\(AP_{\text{AD}}\)(\%)** \\ **Mod.** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Pedestrian—\(AP_{\text{AD}}\)(\%)** \\ **Hard** \\ \end{tabular} } \\  & & **(Hz)** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **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 & - & 88.40 & 77.43 & 70.22 & - & - & - & - & - \\ EPNet [32] & 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 & 77.40 & 70.24 & - & - & - & - & - \\ STD [36] & Lidar only & 12.5 & 87.95 & 79.71 & 75.09 & 78.69 & 61.59 & 55.30 & 53.29 & 42.47 & 38.35 \\ SA-SSD [4] & Lidar only & 25.0 & 88.75 & 79.79 & 74.16 & - & - & - & - & - \\ PV-RCNN [11] & Lidar only & 8.9 & 90.25 & 81.43 & 76.82 & 78.60 & 63.71 & 57.65 & 52.17 & 43.29 & 40.29 \\ \hline ours & Lidar only & 14.2 & 87.91 & 80.09 & 76.28 & 80.17 & **65.74** & **58.32** & 45.47 & 39.39 & 36.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison on the KITTI test set. The AP with 40 recall positions (R40) is selected to evaluate the 3D object detection. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Modality**} & \multirow{2}{*}{ \begin{tabular}{c} **FPS** \\ **(Hz)** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Car—\(AP_{\text{AD}}\)(\%)** \\ **Hard** \\ \end{tabular}  } & \multicolumn{2}{c}{\ \begin{tabular}{c} **Cyclist—\(AP_{\text{AD}}\)(\%)** \\ **Hard** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Pedestrian—\(AP_{\text{AD}}\)(\%)** \\ **Hard** \\ \end{tabular}  } \\  & & **(Hz)** & **Easy** & **Mod. ** & **Hard** & **Easy** & **Mod. ** & **Hard** & **Easy** & **Mod.