# 3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds
## 4 Experimental Results  
We evaluated our 3D object detector on KITTI benchmarks [8] for 3D car detection. We performed the experiment on a TITAN X GPU and developed the code with PyTorch version 1.1. For each image, evaluation is required for 0.005 s. Our experiment was based on Frustum ConvNet [1] and tested the vehicles using the KITTI dataset [8]. We applied the attention module in PointNet and added a parallel Multilayer Perceptron (MLP) architecture in the CBAM attention module with the tanh activation function. The number of parameters in Frustum ConvNet [1] is 3,340,089, and the number of parameters in FCAM is 3,351,353. To reduce the network's model size and prevent overfitting, we used two attention modules and increased the reduction ratios from 16 to 32. A larger reduction ratio reduces parameter overhead and improves the speed of our method. KITTI: The KITTI dataset [8] contains 7481 training pairs, 7518 testing pairs of RGB images, and corresponding point clouds. Following an existing work [15], we split the original training set into the new training and validation sets of 3712 and 3769 samples, respectively. Learning rates start from 0.001 and decay by a factor of 10 every 22 nd epoch of the total 50 epochs. Metrics: We evaluated 3D object proposals using 3D box recall as the metric. For 3D localization, we projected the 3D boxes to the ground plane. We used 3D object detection AP to evaluate the accuracy of the 3D object detection. We used BEV object detection AP to evaluate the accuracy of the BEV object detection. We will now explain the experimental ablation results. Tables 1 and 2 show the detection performance of the improved CBAM on the KITTI validation set for 3D object detection and Bird's Eye View (BEV) object detection, respectively. T means the running time to process each image. Compared with Frustum ConvNet [1] + CBAM [7], our results showed improved AP by 0.09% in the BEV object detection of easy categories. For the 3D object detection, our results showed improved AP by 0.15%, 0.13% in moderate and hard categories. Because the attention model combines two activation functions, our attention module has slightly improved the accuracy, but the running time is longer than before. Figure 4 shows the convergence curves of Frustum, Frustum + CBAM, and Frustum + Improved CBAM. Frustum + Improved CBAM can achieve the highest accuracy on average among these three methods. Tables 3 and 4 show the detection performance of our proposed FCAM on the KITTI validation set for 3D object detection and Bird's Eye View (BEV) object detection, respectively. Compared with Frustum ConvNet [1], our results showed improved AP by 0.21%, 0.27%, and 0.01%, respectively, in the BEV object detection of easy, moderate, and hard categories. For the 3D object detection, our results showed improved accuracy in easy, moderate, and hard cases by 0.27%, 0.43%, and 0.36%, respectively. However, it can be seen that our method did not achieve the best results in 3D hard detection. The reason is that the attention model is targeted at objects with obvious image features. When the features are not obvious and occluded, the detection results can be \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Easy** & **Moderate** & **Hard** \\ \hline MV3D [12] & 71.29 & 62.68 & 56.56 \\ VoxelNet [15] & 81.97 & 65.46 & 62.85 \\ F-PointNet [2] & 83.76 & 70.92 & 63.65 \\ IPDO [26] & 84.1 & 76.4 & 75.3 \\ AVOD-FPN [27] & 84.41 & 74.44 & 68.65 \\ PointRCNN [28] & 88.88 & 78.63 & 77.38 \\ Frustum ConvNet [1] & 89.02 & 78.80 & 77.09 \\ FCAM (Ours) & **89.29** & **79.23** & **77.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Three-dimensional (3D) object detection AP (%) on the KITTI dataset. The best result are in bold. Figure 4: The validation accuracy of Frustum, Frustum + CBAM, and Frustum + Improved CBAM.