# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.4.2 Results and Analyses  
We conducted a comparative analysis of our proposed method against established depth estimation techniques and assessed the algorithm's resilience by utilizing the NYU Depth-v2 Dataset, which comprises indoor scenes, and the KITTI Dataset, which includes outdoor scenes. It is noteworthy that the raw depth within the depth estimation dataset exhibits sparsity, a characteristic attributed to the constraints of depth cameras and LiDAR technology. The ground truth in the dataset was interpolated for visualization purposes, which is only used for comparative analysis and does not represent authentic training label data. The results are presented in Table 4.  
The results demonstrate that the proposed method surpasses other mainstream depth estimation methods such as Eigen, Make3D, DORN, BTS and DDP in all evaluation metrics on the NYU Depth-v2 Dataset. The accuracy index demonstrates a 5% to 50% enhancement in comparison to alternative algorithms, while the error index shows a reduction by 10.8% to 79%. Figure 12: Depth estimation results on NYU Depth-v2 Dataset using different loss functions. Figure 13 illustrates the depth estimation results of our approach on the NYU Depth-v2 Dataset. In Scene 7, our algorithm demonstrates a high level of accuracy in discerning the variations in depth of the box suspended on the wall and the chair. This is particularly evident in its ability to differentiate the distance between the backrest and the seat cushion of the chair from the perspective of the camera. Conversely, alternative algorithms may exhibit indistinct boundary information while processing the depth of the chair. In Scene 9 and Scene 13, it is evident that our method is capable of extracting edge features of small and delicate objects in the scene, such as chandeliers and projectors, enabling the distinction of depth variances between the foreground and background. In Scene 12, characterized by a complex scene depth with various objects placed on the table, our algorithm effectively categorizes the depths of objects in close proximity and those farther away. It successfully identifies clear boundaries and provides accurate depth information, a capability that sets it apart from other algorithms that struggle to differentiate between these depths. Notably, our method demonstrates the ability to distinguish the depth between the bracket on the wall and the wall itself in Scene 8. In Scene 13, the design on the bag is deceptive, leading to inaccurate depth estimations by other algorithms, whereas our approach effectively manages this issue. The outstanding results achieved on the NYU Depth-v2 Dataset served as a catalyst for our decision to proceed with experiments on the KITTI Dataset, yielding impressive outcomes. Table 5 presents a comparison of the results obtained by our method with those of state-of-the-art depth estimation algorithms on the KITTI Dataset. It is evident that the proposed method remains effective for outdoor scenes. The algorithm's accuracy metrics are comparable to those of the BTS algorithm and superior to other algorithms. In the context of error metrics, our approach demonstrates superior performance compared to all other assessed algorithms. It reduces the RMSE from the optimal value of 2.355 to 1.951 and the Abs Rel from the optimal value of 0.120 to 0.095,