# Stereo 3D Object Detection Using a Feature Attention Module
## 4 Experiment
### Pseudo-Lidar F-PointNet Local Fusion and Improvement Strategy  
In this part of the experiment, this study carried out feature fusion in the foreground and background segmentation module of Pseudo-Lidar F-PointNet [5,6]. Based on the fusion method described in Section 3.2, training was performed for 100 epochs, the model used 0.001 as the initial learning rate which decayed at a rate of 0.1 at the 40 th and 80 th epochs, and we only compared with the model with the point cloud data; the experiment results are shown in Table 3. We obtained the bird's-eye view/3D detection accuracy of the three categories. During the experiment, for each object to be detected, a total of 1024 points in the foreground and background were selected, of which 512 points were sampled (downsampled or resampled) in the foreground area. For point cloud data in the form of pseudo-lidar, the feature fusion was performed in the foreground-background segmentation module, and the detection effect was improved to a greater extent. Our first guess for why this occurred is that it is related to the important impact of image semantic information on the segmentation module. For the three categories, the detection results were significantly improved. Similarly, for the segmentation module, this study used the feature attention module to observe different feature weights within the image block. We inserted it into the feature fusion module, obtained the sum of the weights of two kinds of features, divided this by the number of the point cloud, and then obtained the average of all the scenes in the dataset. The results are shown in Table 4. And, we compared the distribution of features that are more important to the point cloud between the two kinds of features. The distribution was visualized, as shown in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category/Feature** & **Point Cloud** & **Image** \\ \hline Car & 0.67 & 0.33 \\ Pedestrian & 0.79 & 0.21 \\ Cyclist & 0.81 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The weight of feature attention module. Figure 8: Fusion feature distribution map within image blocks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Pseudo-Lidar F-PointNet**} & \multicolumn{3}{c}{**Pseudo-Lidar F-PointNet with Late Fusion**} \\ \cline{2-5}  & **Easy** & **Moderate** & **Difficult** & **Easy** & **Moderate** & **Difficult** \\ \hline \multirow{3}{*}{Car} & 71.69/ & 47.77/ & 39.43/ & **75.06/** & **51.09/** & **43.82/** \\  & 57.58 & 35.16 & 29.19 & **62.04** & **38.30** & **32.17** \\ \cline{1-1}  & 43.63/ & 35.01/ & 29.37/ & **51.58/** & **41.55/** & **34.40/** \\ \cline{1-1}  & 33.43 & 25.65 & 21.41 & **42.67** & **33.59** & **27.83** \\ \cline{1-1} Cyclist & 48.31/ & 27.67/ & 25.60/ & **55.20/** & **31.03/** & **28.72/** \\ \cline{1-1}  & 40.83 & 23.28 & 21.62 & **47.49** & **26.47** & **24.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection results based on Pseudo-Lidar F-PointNet. For the image block shown in Figure 9a, the area with a higher proportion of point cloud and image features is shown in Figure 9b, where the points with a higher image weight are colored, and the points with a higher point cloud weight are shown in white. The foreground and background segmentation results from the ground truth are shown in Figure 9c, where green represents the area segmented as foreground, and white represents the area segmented as background. After comparing Figure 9b,c, we found that the area with a higher proportion of image features shown in Figure 9b and the foreground result of the foreground-background segmentation shown in Figure 9c show a similarities. The area in Figure 9b,c is the location area automatically determined in the Cloud Compare tool. Their similarities and differences can be observed from three different perspectives, as shown in Figure 9d-f.