# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 4 Experiments and Results
### Ablation Studies
#### 4.2.1 The Fusion Mode  
References [6; 7; 32] have different conclusions for three fusion modes. [PERSON] et al. [34] considers that the late fusion mode is best, while MV3D [6] proposes the deep fusion mode, and AVOD [7] considers that the early fusion mode is best. Considering the divergence on the conclusions of fusion modes, the performance of different fusion modes was compared in this paper. The results are shown in the first to third rows of Table 3.  
It can be concluded that the performance of the late fusion mode was only 68.06% AP on the moderate setting, which was evidently lower than that on the early and deep fusion mode. At the same time, the performance of the deep fusion mode was only higher by 0.4% and 0.21% than that of early mode on the easy and hard settings, respectively, and even lower by 0.14% on the moderate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**AP\({}_{\text{3D}}\) (IoU = 0.7) (\%)**} \\ \cline{2-5} \multicolumn{1}{c}{**Loss Weight Mask**} & \multicolumn{1}{c}{**Easy**} & \multicolumn{1}{c}{**Mod.**} & \multicolumn{1}{c}{**Hard**} \\ \hline
[1:5:2], [1:1:1:1:1], [1:... :1:1:1] & 84.92 & 75.60 & 68.70 \\
[1:5:2], [1:1:2:1:2], [1:... :1:2:2] & 84.65 & 75.46 & 68.66 \\
[1:5:2], [1:1:3:1:1:3], [1:... :1:3:3] & 85.38 & 76.82 & 69.34 \\
[1:5:2], [1:1:4:1:14], [1:... :1:4:4] & 84.66 & 76.33 & 69.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison results of different weight masks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Architecture**} & \multicolumn{1}{c}{**BEV Channel**} & \multicolumn{1}{c}{**Network**} & \multicolumn{1}{c}{**Loss Mask**} & \multicolumn{3}{c}{**Easy (\%)**} & \multicolumn{3}{c}{**Moderate (\%)**} & \multicolumn{3}{c}{**Hard (\%)**} \\ \cline{3-10} \multicolumn{1}{c}{} & **AP** & **AHS** & **AP** & **AHS** & **AP** & **AHS** \\ \hline Baseline Network & 6 & FPN+FC & ✕ & 83.87 & 83.81 & 74.19 & 73.97 & 67.92 & 67.64 \\ Seven-channel BEV & 7 & FPN+FC & ✕ & 83.96 & 83.69 & 74.23 & 73.82 & 67.59 & 67.44 \\ FDCA Only & 6 & FDCA & ✕ & 85.04 & 84.98 & 75.16 & 74.96 & 68.44 & 68.21 \\ Seven-channel BEV & 7 & FDCA & ✕ & 84.92 & 84.83 & 75.40 & 75.18 & 68.70 & 68.42 \\ and FDCA & 7 & FDCA & ✓ & 85.38 & 85.28 & 76.82 & 76.57 & 69.34 & 69.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of the ablation studies using a KITTI validation set at a 0.7 3D IoU.  
Figure 12: Qualitative results of AVOD-FPN (top) and our FDCA (bottom). **Left**: 2D bounding boxes output in the image plane, **Middle**: 3D bounding boxes output in the image plane, and **Right**: 3D bounding boxes in the 3D LiDAR point cloud space. The red bounding boxes were the ground truth, the green bounding boxes were the prediction results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Fusion Mode**} & \multicolumn{3}{c}{**AP\({}_{\text{3D}}\) (IoU = 0.7) (\%)**} \\ \cline{3-6}  & & **Easy** & **Mod.** & **Hard** \\ \hline \multirow{3}{*}{Baseline Network} & Early & 83.87 & 74.19 & 67.92 \\  & Late & 77.92 & 68.06 & 67.30 \\ \cline{1-1}  & Deep & 84.27 & 74.05 & 68.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of fusion modes using the KITTI validation set.  
setting while undertaking more computation and having more parameters. Therefore, in the follow-up studies, early fusion mode was adopted in the second-stage refinement network.