# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 4 Experiment and Results
### Ablation Experiment  
In order to investigate the contribution of various improvement methods to the network, we conducted ablation experiments on three class detection tasks. From the data in Table 4, it can be seen that D-fusion, AFM and SENet have all improved network performances. Moreover, in the car detection tasks, the combination of the three can achieve the best overall results. In the detection task of pedestrians and cyclists, the three together failed to achieve the optimal effect on each task, which is due to a certain degree of overfitting caused by the deepening of the network. Pay attention to network complexity as well as performance improvement. Table 5 shows the number of parameters in each part of the network. Thus, the introduction of the SENet and AFM modules increases the number of parameters by a relatively small amount.  
\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 D-fusion & & & 83.99 & 74.93 & 68.08 & 89.41 & 80.12 & 79.21 \\ AFM & & & 84.52 & 74.60 & 68.00 & 89.33 & 86.37 & 79.32 \\ SENet & & & 82.90 & 73.40 & 66.98 & 88.59 & 85.73 & 78.65 \\ D-fusion and AFM & & & 85.11 & **75.70** & 68.64 & 89.19 & 86.56 & 79.29 \\ D-fusion and SENet & & & 84.45 & 74.80 & 67.64 & 89.53 & 80.13 & 79.05 \\ AFM and SENet & & & 85.12 & 75.65 & 68.78 & **89.74** & 86.66 & 79.39 \\ D-fusion and AFM and SENet & & & **85.58** & 75.37 & **68.83** & 89.59 & **86.72** & **79.52** \\ \hline D-fusion & & & 53.98 & 48.44 & 41.90 & 57.95 & 51.97 & 45.09 \\ AFM & & & **59.27** & **52.56** & **46.14** & 61.35 & 54.54 & 47.64 \\ SENet & & & 47.33 & 41.23 & 35.29 & 55.40 & 48.57 & 42.27 \\ D-fusion and AFM & Ped & & 53.98 & 48.78 & 42.87 & 57.13 & 51.41 & 45.09 \\ D-fusion and SENet & & & 53.84 & 50.65 & 45.14 & 58.79 & 53.42 & 47.65 \\ AFM and SENet & & & 54.02 & 43.79 & 41.83 & 54.83 & 49.23 & 43.23 \\ D-fusion and AFM and SENet & & & 53.87 & 51.27 & 45.99 & **62.00** & **56.19** & **49.97** \\ \hline D-fusion & & & 68.07 & 41.84 & 41.22 & 58.61 & 40.46 & 39.90 \\ AFM & & & 66.65 & 40.97 & 40.46 & 67.21 & 40.80 & 40.51 \\ SENet & & & 48.48 & 31.25 & 25.66 & 49.16 & 31.75 & 25.92 \\ D-fusion and AFM & Cyc & **69.11** & **43.42** & **42.40** & **69.72** & 43.95 & **42.95** \\ D-fusion and SENet & & & 61.19 & 41.38 & 34.57 & 61.70 & 41.67 & 34.81 \\ AFM and SENet & & & 67.93 & 42.11 & 41.07 & 68.53 & 42.31 & 41.92 \\ D-fusion and AFM and SENet & & & 68.66 & 42.23 & 41.71 & 68.28 & **46.48** & 40.64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study. The best scores are highlighted in bold.  
\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 FPN & & 83.25 & 74.55 & 67.46 & 89.24 & 86.57 & 78.81 \\ AFM and SENet & Car & 84.71 & 74.79 & 68.17 & **89.77** & **86.84** & 79.34 \\ AFM and SENet & & **85.12** & **75.65** & **68.78** & 89.74 & 86.66 & **79.39** \\ \hline FPN & & 47.33 & 41.23 & 35.29 & 55.40 & 48.57 & 42.27 \\ AFM & Ped & **61.32** & **54.47** & **47.62** & **59.30** & **52.60** & **46.17** \\ AFM and SENet & & 50.69 & 45.17 & 39.88 & 57.01 & 50.37 & 44.09 \\ \hline FPN & & 48.48 & 31.25 & 25.66 & 49.16 & 31.75 & 25.92 \\ AFM & Cyc & 66.65 & 40.97 & 40.46 & 67.21 & 40.80 & 40.51 \\ AFM and SENet & & **68.03** & **41.88** & **41.45** & **69.20** & **42.71** & **41.74** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average precision comparison of different feature extractors on the KITTI validation set. The best scores are highlighted in bold.