## 4 Experiments
### Experimental Evaluation  
#### 4.2.1 Depth Evaluation  
We take the split provided by [10] to compare with prior related methods to evaluate the performance of our network in monocular depth estimation. We exclude the visually similar frames to the test scenes according to [15]. The predictions are resized at input image resolution by interlinear interpolation. We use ground truth obtained by a laser scanner. Both 50 m and 80 m threshold of maximum depth for evaluation are used. It takes two consecutive frames during training because of the 3D alignment loss. We compare with both supervised methods and state-of-the-art unsupervised methods. We also compare with the results of [PERSON] et al. [21] which takes stereo pairs to predict depth. Some qualitative results are shown in Figure 3. The metrics in Tables 1-3 show the quantitative results of all standard error measures of these methods. To verify the advantage of the network architecture and the effectiveness of our loss terms, we trained \"Ours(VGG)\" on KITTI which shares the same structure with [15]. The results validated our conclusion. Furthermore, \"Ours(ResNet)\" significantly outperforms both supervised works [10; 27] and prior unsupervised baselines [15; 18; 19] with a reasonable margin, which is benefited from our extensive geometric constraint and mutually supervised mechanism. We also experimented with pre-training on Cityscapes first. As same as [19], our result is slightly inferior to [11] when trained on Cityscapes and KITTI due to characteristics of training data. We believe that such dataset stereo settings may provide more details, which we would like to explore in the future. Note that during our training process, robust similarity technique like SSIM [28] are not adopted with which should lead to further improvement. We also conduct Ablation Experiments on the components of our network in Table 4. In order to study the importance of each component, we trained and evaluated a series of models with each one component missing. The study shows our approach is able to benefit from all these components, and 3D losses make a great difference. On the other hand, a visualized comparison to failed cases of Zhou [15] in Figure 4. further illustrates our method's capability of reasoning scene geometry. However, there are still failure cases remained when large dynamic objects close to camera or intense lighting changes happened caused by gradient locality. The search for a solution to the gradient locality of warping-based loss is still in need. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Supervise** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE Log** & \(\delta<1.25\) & \(\delta<1.25^{2}\) & \(\delta<1.25^{3}\) \\ \hline [PERSON] [10] Coarse & Depth & 0.214 & 1.605 & 6.563 & 0.292 & 0.673 & 0.884 & 0.957 \\ [PERSON] [10] Fine & Depth & 0.203 & 1.548 & 6.307 & 0.282 & 0.702 & 0.890 & 0.958 \\ [PERSON] [27] & Depth & 0.202 & 1.614 & 6.523 & 0.275 & 0.678 & 0.895 & 0.965 \\ [PERSON] [11] & Stereo & 0.148 & 1.344 & 5.927 & 0.247 & 0.803 & 0.922 & 0.964 \\ [PERSON] [15] & No & 0.208 & 1.768 & 6.856 & 0.283 & 0.678 & 0.885 & 0.957 \\ [PERSON] [18] & No & 0.163 & 1.240 & 6.220 & 0.250 & 0.762 & 0.916 & 0.968 \\ [PERSON] [19] & No & 0.155 & 1.296 & 5.857 & 0.233 & 0.793 & 0.931 & 0.973 \\ Ours (VGG) & No & 0.157 & 1.229 & 5.960 & 0.238 & 0.799 & 0.932 & 0.973 \\ Ours (ResNet) & No & 0.149 & 1.223 & 5.732 & 0.225 & 0.829 & 0.944 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Monocular depth results on KITTI by the split of [PERSON] et al. [10] capped 80 m. Results of other methods are taken from [15; 18; 19].