# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 4 Experiment and Results
### The Effect of AFM and SENet  
For the feature extractors, we compared three scenarios: traditional convolution with FPN, traditional convolution with AFM and traditional convolution with AFM and SENet, as shown in Table 3. For the car class, traditional convolution with AFM and SENet has a certain effect on AP\({}_{\text{3D}}\), but on the AP\({}_{\text{BEV}}\), the difference is very small compared to traditional convolution with AFM, indicating that the attention mechanism does not play a significant role in the BEV. For the pedestrian categories, traditional convolution with AFM and SENet is significantly better than traditional convolution with FPN, but it is also significantly worse than traditional convolution with AFM. We analyzed that the reason for this result is that there are many small object pedestrian samples, and factors such as human posture cause significant sample differences. For the cyclist class, the results of traditional convolution with AFM and SENet are significantly better than those of traditional convolution with FPN and also better than traditional convolution with AFM. It is worth noting that compared to traditional convolution with FPN, traditional convolution with AFM and SENet is about 20% higher at the easy level, at least 10% higher at the moderate level and about 16% higher at the hard level.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**AP\({}_{\text{3D (\%)}}\)**} & \multicolumn{3}{c}{**AP\({}_{\text{BEV (\%)}}\)**} \\ \cline{3-8}  & & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **MV3D** \\ **AVOD-FPN** \\ **F-PointNet** \\ **Ours** \\ \end{tabular} } & 61.22 & 48.36 & 44.37 & 66.70 & 54.76 & 50.55 \\  & 60.11 & 44.90 & 38.80 & 63.66 & 47.74 & 46.55 \\  & 64.00 & 52.18 & 46.61 & 68.09 & 57.48 & 50.77 \\  & **71.96** & **56.77** & **50.39** & **75.38** & **61.96** & **54.68** \\  & 68.66 & 42.23 & 41.71 & 68.28 & 46.48 & 40.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**AP\({}_{\text{3D (\%)}}\)**} & \multicolumn{3}{c}{**AP\({}_{\text{BEV (\%)}}\)**} \\ \cline{3-8}  & & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Early fusion** \\ **Late fusion** \\ **Deep fusion** \\ **D-fusion (Ours)** \\ \end{tabular} } & 82.12 & 73.87 & 67.70 & 88.50 & 86.07 & 79.03 \\  & 70.28 & 56.48 & 55.85 & 86.47 & 77.17 & 70.03 \\  & 82.86 & 73.42 & 67.30 & 89.07 & 85.91 & 78.99 \\  & **85.58** & **75.37** & **68.83** & **89.59** & **86.62** & **79.52** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Early fusion** \\ **Late fusion** \\ **Deep fusion** \\ **D-fusion (Ours)** \\ \end{tabular} } & 45.60 & 40.75 & 35.07 & 48.71 & 43.42 & 37.23 \\  & 48.77 & 43.75 & 37.20 & 52.64 & 46.55 & 45.16 \\  & 47.16 & 40.85 & 35.21 & 54.43 & 47.69 & 41.83 \\  & **53.87** & **51.27** & **45.99** & **62.00** & **56.19** & **49.97** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Early fusion** \\ **Late fusion** \\ **Deep fusion** \\ **D-fusion (Ours)** \\ \end{tabular} } & 49.62 & 32.1 & 31.57 & 50.09 & 32.49 & 31.89 \\  & 65.19 & 40.71 & 40.32 & 65.72 & 41.35 & **40.73** \\  & 46.22 & 29.07 & 23.66 & 47.16 & 29.66 & 29.37 \\  & **68.66** & **42.23** & **41.71** & **68.28** & **46.48** & 40.64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average precision comparison of different fusions on the KITTI validation set. The best scores are highlighted in bold.