## 4 Experimental Results
### Performance Evaluation Results  
For the experiments, we adopt MobileNet-s as the encoder. We use the official testing split, which contains 654 testing samples, to test the performance by using average evaluation metrics. The resolution of RGB images is set to 224 \(\times\) 224 and the resolution of simulated LRLQ depths is set to 8 \(\times\) 8. We compare the proposed models to FastDepth [10] with ResNet50 + NNConv5, FastDepth [10] with MobileNet + NNConv5 and MonoDepth [13] with ResNet50 + DispNet for 224 \(\times\) 224 RGB input images. As Table 5 shown, the FastDepth [10] with ResNet50+NNConv5 and the MonoDepth [13] with ResNet50 + DispNet respectively require about 0.74 G MACs and 4.8 G MACs for 224 \(\times\) 224 depth map completion. In our proposed network, the Dual MobileNet encoder including six depthwise separable convolution layers and one pointwise convolution totally requires about 0.185G MACs. In Table 5, three proposed models using the solo-SimpleUp decoder, the SimpleUp + AdaDRBins decoder and the UpCSPN-7 + AdaDRBins decoder are chosen to display their computational complexities in order. These three models demand 0.675 (i.e., 0.185 + 0.49), 1.335 (i.e.,0.185 + 1.15) and 9.445 (i.e.,0.185 + 9.26) G MACs, respectively. The proposed simplest model, which adopts the solo-SimpleUp decoder, is not only lighter than the fastest version of FastDepth in MACs but also achieves a lower 0.233 RMSE than the 0.599 RMSE obtained for testing the NYU Depth V2 dataset. It is noted that MonoDepth tested on the KITTI dataset attains 4.392 RMSE in monotonous outdoor scenes. Thus, for the more complex indoor dataset, e.g., the NYU Depth V2 dataset, the RMSE of MonoDepth shall be larger than 4.392, such that we enlisted the RMSE of MonoDepth as >4.392 in Table 5.  
In Figure 12, the qualitative results of our proposed network on the NYU Depth V2 dataset are displayed in a visualization. We observe that the depth maps achieved by the combination of the UpCSPN-k and AdaDRBins modules preserve more details and clearer boundaries than those obtained by SimpleUp. However, the depth maps that resulted from the model with the solo-SimpleUp decoder are still clear and recognizable. For considering the balance of complexity and precision, the combination of SimpleUP and AdaDRBins could be a good solution for resource-limited platforms. Particularly, observing the second and fourth rows of Figure 12, the rectangle-highlighted depth areas resulting from the proposed network using UpCSPN-7 + AdaDRBins decoder can be even better than the corresponding depth areas in GT maps in terms of visual rationality.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Encoder** & **Decoder** & \begin{tabular}{c} **Weights** \\ **(M)** \\ \end{tabular} & \begin{tabular}{c} **MAC** \\ **(G)** \\ \end{tabular} & **RMSE** \\ \hline FastDepth [10] & ResNet50 & NNConv5 & 25.60 & 4.190 & 0.568 \\ FastDepth [10] & MobileNet & NNConv5 & 3.19 & 0.740 & 0.599 \\ MonoDepth [13] & ResNet50 & DispNet & 30.00 & 4.800 & \textgreater{}4.392 \\ Proposed & DMobileNet\_s & SimpleUp & **2.18** & **0.675** & 0.223 \\ Proposed & DMobileNet\_s & \
\begin{tabular}{c} SimpleUp+ \\ AdaDRBins \\ \end{tabular} & 2.28 & 1.150 & 0.199 \\ Proposed & DMobileNet\_s &
\begin{tabular}{c} UpCSPN-7+ \\ AdaDRBins \\ \end{tabular} & 43.57 & 9.445 & **0.185** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of FastDepth, MonoDepth and the various proposed variants by using the NYU Depth V2 dataset.  
Through our experiments, the depth completion accuracy of 32 bins is very close to those of 64 bins, 128 bins and 256 bins for 224 \(\times\) 224 depth maps while adopting AdaDRBins in the proposed architecture. It means that the use of 32 bins provided by AdaDRBins, i.e., 32-bin AdaDRBins, could be the most appropriate under the consideration of model complexity for our proposed models. The computational complexity comparisons of 32-bin AdaDRBins and 256-bin AdaBins [12] are listed at Table 6.