## 5 Results
### Results Obtained in KITTI Depth Completion Suite  
We processed the complete KITTI data set and obtained the \(MSE\) and \(MAE\). In Table 6, we show the \(MSE\) and \(MAE\) for our proposal, as well as for other models, such as Deep Fusion, Morphological Operators neural networks [23], and Convolutional Spatial Propagation Networks [24].  
Table 6 shows the quantitative results of our proposal compared to other state-of-the-art models. Our three proposals outperform our previous work, already presented  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **MAE** & **MSE + MAE** \\ \hline Proposed model— & 1.1395 & 0.3127 & 1.4522 \\ Texture+Structure & & & \\ Proposed & & & \\ model—anisotropic & 1.1269 & 0.3107 & 1.4376 \\ metric & & & \\ Balanced biased & 1.1254 & 0.3205 & 1.4459 \\ infinity Laplacian & & & \\ biased infinity Laplacian & 1.1256 & 0.3223 & 1.4479 \\ Presented results in & & & \\ PDAA [1] & 1.1397 & 0.3132 & 1.4529 \\ Conv. Spatial. Prop. & & & \\ Networks [24] & 1.0196 & 0.2795 & 1.2991 \\ Morpho. & & & \\ Networks [23] & 1.0455 & 0.3105 & 1.3560 \\ Deep Fuse & & & \\ Networks [13] & 1.2067 & 0.4299 & 1.6366 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Depth Completion Error in KITTI data set.  
Figure 13: Examples of lack of information in depth maps. Figures (**a–c**) show reference color images. Figures (**d–f**) show depth map ground truth.  
in [1], performing better than the model considering the Texture+Structure decomposition. Comparing our results with other models, we observe that our proposals outperform Deep Fuse Networks [13] and perform similarly to Convolutional Spatial Propagation Networks [24] and Morpho Networks [23]. We emphasize that our proposals are straightforward and fast to implement. Completing \(u(\mathbf{x})\) involves a simple weighted average and an iterative procedure.  
Table 6 shows that the proposed anisotropic metric model is our best proposal considering MSE+MAE. It is noteworthy that the model presenting the best performance is the simplest one, i.e., infinity Laplacian and the anisotropic metric.  
In Figure 14, we show qualitative results obtained by our models in the KITTI data set. Figure 14e,f show the minimum MSE obtained by our model in the KITTI data set, with an error of 53.78 cm and 52.09 cm, respectively.  
In Figure 15, we show qualitative results where our model fails to complete the task. In Figure 15g-i, we observe that our model cannot recover the car geometry due to transparent objects or excessive reflections. In these three images, we obtained the most significant \(MSE\) errors. In (g) we obtained \(MSE=3.2025\), in Figure 15h we obtained \(MSE=3.1126\), and in Figure 15i we obtained \(MSE=2.7428\).  
Figure 14: Examples of qualitative and quantitative results obtained by our best model in the KITTI data set. We show color reference images in (**a**,**b**). In (**c**,**d**), we show the sparse depth map used as input to our model. In (**e**,**f**), we show the completed depth map and the \(MSE\) obtained in each image.  
Figure 15: Examples of qualitative and quantitative errors obtained by our model in the KITTI data set. We show our worst results in the KITTI data set. In (**a**–**c**), we show color reference images. We present the sparse depth map used as input to our model in (**d**–**f**). In (**g**–**i**), we show the completed depth map.