## 4 Experiments
### Evaluation on KITTI Dataset
#### 4.2.1 Quantitative Results  
We compared the test results of our model with other works trained with different types of unsupervised models. All models have been trained on the Eigen split of the KITTI 2015 driving dataset. Table 2 shows the quantitative results. \(N\), \(\hat{d}_{i}\), and \(d_{i}\) represent the total number of pixels, predicted depth value, and ground-truth depth value for pixel \(i\), respectively. For the quantitative evaluation, we used several standard evaluation metrics, which are as follow. 1. Absolute relative error (_Abs Rel_): \(\frac{1}{N}\sum_{i=1}^{N}\frac{||\hat{d}_{i}-d_{i}||}{\hat{d}_{i}}\). 2. Squared relative error (_Sq Rel_): \(\frac{1}{N}\sum_{i=1}^{N}\frac{||\hat{d}_{i}-d_{i}||^{2}}{\hat{d}_{i}}\). 3. Root-mean-squared error (_RMSE_): \(\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{d}_{i}-d_{i})^{2}}\). 4. Mean \(log10\) error (_RMSE log_): \(\sqrt{\frac{1}{N}\sum_{i=1}^{N}||log(\hat{d}_{i})-log(d_{i})||^{2}}\). 5. Accuracy with threshold \(t\), that is, the percentage of \(\hat{d}_{i}\) such that \(\delta=max(\frac{d_{i}}{d_{i}},\frac{\hat{d}_{i}}{\hat{d}_{i}})<t\), where \(t\in[1.25,1.25^{2},1.25^{3}]\). For (1)-(4), the lower the score, the better the results. For (5), the higher the score, the better the outcome. In category S, the category of our model, the best results are highlighted in bold red. The second-best results are underlined blue. Our model learns how to generate disparity maps needed for the image-to-image transformation process in an unsupervised model based on stereo images (S). As compared to models that were similarly trained, our model has superior performance on all metrics, except for absolute relative error, and achieved second-best in the last metric. In particular, it shows better results than Monodepth [6], which has a similar network structure to our \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**Type** & **Method** & _Abs Rel_ & _Sq Rel_ & _RMSE_ & _RMSE log_ & \(\delta<1.25\) & \(\delta<1.25^{2}\) & \(\delta<1.25^{3}\) \\ \hline \multirow{4}{*}{M} & [PERSON] [20] & 0.183 & 1.595 & 6.709 & 0.270 & 0.734 & 0.902 & 0.959 \\  & Yang [22] & 0.182 & 1.481 & 6.501 & 0.267 & 0.725 & 0.906 & 0.963 \\  & Mahjourian [23] & 0.163 & 1.240 & 6.220 & 0.250 & 0.762 & 0.916 & 0.968 \\  & GeoNet [24] & 0.149 & 1.060 & 5.567 & 0.226 & 0.796 & 0.935 & 0.975 \\  & DDVO [25] & 0.151 & 1.257 & 5.583 & 0.228 & 0.810 & 0.933 & 0.974 \\  & EPC++ [27] & 0.141 & 1.029 & 5.350 & 0.216 & 0.816 & 0.941 & 0.979 \\ \hline \multirow{4}{*}{S} & Garg [28] & 0.152 & 1.226 & 5.849 & 0.246 & 0.784 & 0.921 & 0.967 \\  & Godard [6] & 0.148 & 1.344 & 5.927 & 0.247 & 0.803 & 0.922 & 0.964 \\  & SuperDepth+pp [29] (\(1024\times 382\)) & 0.112 & 0.875 & 4.958 & 0.207 & 0.852 & 0.947 & 0.977 \\  & Monodepth2 [1] (\(640\times 192\)) & **0.109** & 0.873 & 4.960 & 0.209 & 0.864 & 0.948 & 0.975 \\  & Park+pp [7] & 0.121 & 0.836 & 4.808 & 0.194 & 0.859 & 0.957 & **0.982** \\  & **Ours** & 0.116 & 0.873 & 4.805 & 0.198 & 0.869 & 0.953 & 0.977 \\  & **Ours+pp** & 0.112 & **0.832** & **4.741** & **0.192** & **0.876** & **0.957** & 0.980 \\ \hline \multirow{2}{*}{MS} & EPC++ [27] & 0.128 & 0.935 & 5.011 & 0.209 & 0.831 & 0.945 & 0.979 \\  & Monodepth2 [1] & 0.106 & 0.818 & 4.750 & 0.196 & 0.874 & 0.957 & 0.979 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative Results: Comparison with unsupervised learning-based monocular depth-estimation models. Unsupervised monocular depth-estimation models are divided into the following categories: (M) Unsupervised model based on video sequence (Monocular supervision; USI model), (S) Unsupervised model based on stereo images (Stereo supervision; USV model), and (MS): Monocular + stereo supervision. model. This demonstrates the importance of a effective loss function, such as a pre-trained PieAPP model. As compared to unsupervised models based on video sequences (M), our model's performance is better.