# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Performance Comparison  
We selected the model that achieved the best performance in the ablation study to compare to models from previous studies on the KITTI dataset. The model from [2; 12; 35] fuses multi-scale features that integrate low-resolution to high-resolution depth maps. LAPD [14] uses a decoder architecture that adopts feature maps of different scales to enhance global structure and local detail depth estimation and combines edge maps with feature maps to improve depth prediction. DORN [7] uses a deep ordinal regression that adopts dilated convolution to obtain high-resolution depth maps. Furthermore, ordinal regression can also solve the problem of slow convergence. As not every image in the KITTI dataset contains foreground regions, we applied two dataset settings to evaluate the depth map prediction performance of our model and the selected previous methods.  
Firstly, we evaluated the depth prediction of images from the KITTI dataset that contain foreground regions and excluded images without foreground regions. Table 4 shows that our model outperformed the previous methods. Our proposed model outperformed the state-of-the-art LAPD model by 0.3% in \(\delta<1.25\) and 0.09 in Sq Rel. Since our proposed model was beneficial for foreground depth prediction, the foreground and background models were separated to avoid interference between the foreground and background depth prediction. Moreover, we used an attention mechanism to extract meaningful features that could be used as clues to enhance foreground depth prediction. Figure 8 shows that our model could contour the details of foreground objects in the depth maps. For example, as shown in the second column, only our method could contour the windows of the SUV in the depth image. Although our proposed model did not offer significant improvements compared to the previous methods, it could still be seen that only our proposed method could depict the details of foreground objects while mitigating interference from significant differences in image depth.  
Secondly, we evaluated our model and the selected previous methods on the entire KITTI dataset, including images that contain foreground regions and those that do not. Table 5 shows the results from the depth map prediction of the images. Our method achieved the best performance out of all tested models. It surpassed LAPD, which is also a method that considers multi-scale features maps, by 0.1% in \(\delta<1.25\) and 0.051 in RMSE. Even though this was only a minor improvement, we still proved that individually predicting foreground and background depth maps is beneficial to depth prediction. Meanwhile, it is also worth mentioning that our model adopted channel-wise and spatial-wise attention modules to extract features that could highlight global and local information and strengthen the background and foreground prediction models individually.  
In addition, we also evaluated the execution times of our model and the previous methods. We randomly selected 30 images from the KITTI dataset for our evaluation of the execution time of depth prediction. [PERSON] [7] and [PERSON] [35] only uploaded the depth  
Figure 7: A visualization of the results from the different stitching strategies: direct stitching and CNN stitching.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stitching** & \(\delta\) **\textless{} 1.25** & \(\delta\) **\textless{} 1.25\({}^{2}\)** & \(\delta\) **\textless{} 1.25\({}^{3}\)** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE log** \\ \hline Direct Stitching & **0.963** & 0.995 & 0.999 & **0.058** & **0.198** & **2.395** & **0.089** \\ \hline CNN Stitching & **0.963** & 0.995 & 0.999 & **0.058** & **0.198** & 2.400 & 0.090 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The evaluation of our depth map stitching strategies.