## 5 Experimental Results
### Effectiveness of the Twice 3D Detection  
Our method is a multi-stage 3D object detection algorithm on the basis of 2D detection. First of all, we perform 2D object detection to obtain the region of interest, estimate the initial 3D box on the feature map generated from the fusion data, and finally use the bird's-eye view to refine the initial 3D box. We set up three groups of experiments to verify the effectiveness of the 3D box regression performed twice. In the first group, we directly estimate the 3D box's parameters while performing the 2D detection. In the second group, on the basis of the 2D detection, we feed the fusion data of the 2D region of interest and the pseudo-lidar to the keypoint estimation network for the initial 3D object detection. In the third group, on the basis of the second group, we utilize the bird's-eye view to refine the coarse initial 3D box to obtain a more accurate 3D box. All the experimental results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{M}\)} & \multicolumn{3}{c}{\(AP_{\text{bw}}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline
2D-based regression & 0.41 & 1.34 & 1.2 & 0.73 & 1.82 & 1.95 \\ \hline + keypoint-based regression & 24.39 & 18.17 & 17.62 & 33.91 & 24.15 & 21.86 \\ \hline + bird’s-eye-based regression & 34.85 & 22.59 & 19.81 & 44.71 & 30.67 & 25.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of 3D object regression.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{M}\)} & \multicolumn{3}{c}{\(AP_{\text{bw}}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline ROI-10D [32] & 4.32 & 2.02 & 1.46 & 9.78 & 4.91 & 3.74 \\ \hline MultiFusion [36] & 7.08 & 5.18 & 4.68 & 13.73 & 9.62 & 8.22 \\ \hline UR3D [45] & 15.58 & 8.61 & 6.00 & 21.85 & 12.51 & 9.20 \\ \hline RTM-3D [8] & 14.41 & 10.34 & 8.77 & - & - & - \\ \hline MonoPair [44] & 13.04 & 9.99 & 8.65 & 19.28 & 14.83 & 12.89 \\ \hline SMOKE [34] & 14.03 & 9.76 & 7.84 & 20.83 & 14.49 & 12.75 \\ \hline D4 LCN [18] & 16.65 & 11.72 & 9.51 & - & - & - \\ \hline Lite-FFN [31] & 15.32 & 10.64 & 8.59 & 26.67 & 17.58 & 14.51 \\ \hline AMSD [17] & 16.50 & 10.74 & 9.52 & - & - & - \\ \hline PatchNet [12] & 15.68 & 11.12 & 10.17 & - & - & - \\ \hline Ours & 15.86 & 11.41 & 10.08 & 28.79 & 18.61 & 15.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of and on KITTI test sets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{M}\)} & \multicolumn{3}{c}{\(AP_{\text{bw}}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline MonoDepth & 22.03 & 17.41 & 13.39 & 26.28 & 23.25 & 19.73 \\ \hline DORN & **34.85** & **22.59** & **19.81** & **44.71** & **30.67** & **25.05** \\ \hline
**PSMNet** & 39.63 & 25.23 & 21.58 & 54.7 & 35.92 & 29.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance difference of three depth estimators.