# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 4 Experiment and Results
### 3D Detection  
For the final 3D detection results, we used two metrics to measure the accuracy of 3D positioning and 3D anchor box detection. For 3D positioning, we projected the 3D box to the ground plane to obtain the BEV anchor frame. We calculated the average accuracy of the BEV anchor frame (AP\({}_{\text{BEV}}\)). For the 3D bounding box, we used the average precision (AP\({}_{\text{3D}}\)) metric to evaluate the complete 3D anchor box.  
When using AP\({}_{\text{3D}}\) and AP\({}_{\text{BEV}}\) for evaluation, we set an IoU threshold of 0.7 for the car class and 0.5 for the pedestrian and bicycle classes. We compared the detection results with the state-of-the-art network publicly available on the validation set. On the validation set, as shown in Table 1, our architecture performs optimally in both car and pedestrian detection. It is worth mentioning that in the comparison of car and pedestrian detection, our architecture is, on average, 3.19% and 5.55% higher than AVOD-FPN on AP\({}_{\text{3D}}\) and the AP\({}_{\text{BEV}}\), respectively.  
Figure 5: Test result diagram. Car, pedestrian and cyclist represent three object classes, with the predicted car represented by a green box in (**a**), pedestrian represented by a turquoise box in (**b**), and cyclist represented by a yellow box in (**c**). The red boxes represent the true value of GroundTruth. The first score represents the confidence score of the 3D prediction box, and the second score represents the intersection over union (IoU) of the 3D prediction box and the GroundTruth.  
Figure 6: Comparison of small object detection results. (**a**) represents the network AVOD, and (**b**) represents the network in this paper. The red boxes represent the GroundTruth. The green boxes represent the predicted results.  
\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{2-9}  & & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline MV3D & & 71.09 & 62.35 & 55.12 & 86.02 & 76.90 & 68.49 \\ AVOD & & 73.59 & 65.78 & 58.38 & 86.80 & 85.44 & 77.73 \\ AVOD-FPN & & 81.94 & 71.88 & 66.38 & 88.53 & 83.79 & 77.90 \\ F-PointNet & & 81.20 & 70.39 & 62.19 & 88.70 & 84.00 & 75.33 \\ SCANet & & 83.63 & 74.47 & 67.78 & - & - & - \\ MVX-Net & & 83.20 & 72.70 & 65.20 & 89.20 & 85.90 & 78.10 \\ ContFuse & & 82.54 & 66.22 & 64.04 & 88.81 & 85.83 & 77.33 \\ CrossFusion & & 83.20 & 74.50 & 67.01 & 88.39 & 86.17 & 78.23 \\ Ours & & **85.58** & **75.37** & **68.83** & **89.59** & **86.62** & **79.52** \\ \hline MV3D & & 39.48 & 33.69 & 31.51 & 46.13 & 40.74 & 38.11 \\ AVOD & & 38.28 & 31.51 & 26.98 & 42.52 & 35.24 & 33.97 \\ AVOD-FPN & Ped & 50.80 & 42.81 & 40.88 & 58.75 & 51.05 & 47.54 \\ F-PointNet & & 51.21 & 44.89 & 40.23 & 58.09 & 50.22 & 47.20 \\ Ours & & **53.87** & **51.27** & **45.99** & **62.00** & **56.19** & **49.97** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average precision of 3D anchor boxes on the KITTI validation set (AP\({}_{\text{3D}}\)) (represented by %) and average precision of BEV anchor boxes (AP\({}_{\text{BEV}}\)) (represented by %). The best scores are highlighted in bold.  
Figure 7: The testing accuracy of the object detection. The following three indicators measure the test results of the car, cyclist and pedestrian. Where object_detection, object_detection_3D and car_detection_BEV represent the AP, where an object represents the car, cyclist or pedestrian.