## 5 Discussion  
In this study, for low-cost MR applications, we proposed depth estimation algorithms with lightweight encoders and a simpleUp multilevel shared decoder. The simplest network with only 2.18 M parameters was successfully implemented in Jorjin JZF Plus AR glasses. It is noted that the proposed network with lightweight encoders, the UpCSPN-7 multilevel shared decoder and AdaDRbins, can achieve the highest precision performance in depth estimation, better than AdaBines-based networks. We can divide the models of proposed network into four groups, whose decoders are Solo-SimpleUp, UpCSPN-k only, SimpleUP + AdaxBins (AdaxBins can be AdaBins and AdaDRBins) and UpCSPN-k + AdaDRBins. Observing Table 7, we find that the RMSEs of models between different groups have salient differences, and the RMSEs of models in the identical group have close RMSEs. Even that, in the second group, i.e., UpCSPN-k only, the UpCSPN-5 only decoder is better than the UpCSPN-7 only decoder. This fact can manifest as evidence for the deep learning investigation that more stacked layers could not guarantee better performance. We can claim that better structure design is more important than the use of larger amounts of kernels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Decoder** & **Bins Estimator** & **RMSE (m)** & **REL** & \(\delta_{1}\) & \(\delta_{2}\) & \(\delta_{3}\) \\ \hline SimpleUp & — & 0.223 & 0.046 & 97.36 & 99.43 & 99.85 \\ UpCSPN-3 & — & 0.206 & 0.039 & 97.87 & 99.56 & 99.89 \\ UpCSPN-5 & — & 0.197 & 0.036 & 97.87 & 99.55 & 99.89 \\ UpCSPN-7 & — & 0.202 & 0.036 & 97.87 & 99.54 & 99.89 \\ SimpleUP & AdaBins & 0.197 & 0.037 & 98.04 & 99.64 & 99.91 \\ SimpleUp & AdaDRBins & 0.199 & 0.038 & 98.06 & 99.64 & 99.91 \\ UpCSPN-3 & AdaDRBins & 0.190 & 0.036 & 98.21 & **99.67** & **99.92** \\ UpCSPN-5 & AdaDRBins & 0.191 & 0.036 & 98.21 & **99.67** & **99.92** \\ UpCSPN-7 & AdaDRBins & **0.185** & **0.034** & **98.24** & **99.67** & **99.92** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative results of the proposed networks achieved by different structures, where REL expresses the relative error and \(\delta_{1}=1.25\), \(\delta^{2}=1.25^{2}\) and \(\delta^{3}=1.25^{3}\) are the error-toleration thresholds of ratios of estimated depth and GT depth. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Decoder** & **Bins Estimator** & **\#Bins** & **Weights (M)** & **MAC (G)** & **FPS** \\ \hline SimpleUp & — & - & **2.18** & **0.49** & **1170** \\ UpCSPN-3 & — & — & 41.85 & 8.04 & 367 \\ UpCSPN-5 & — & — & 42.13 & 8.36 & 356 \\ UpCSPN-7 & — & — & 42.57 & 8.60 & 337 \\ SimpleUp & AdaBins & 256 & 4.52 & 4.43 & 425 \\ SimpleUp & AdaDRBins & 32 & **2.28** & **1.15** & **560** \\ UpCSPN-3 & AdaDRBins & 32 & 42.85 & 8.70 & 340 \\ UpCSPN-5 & AdaDRBins & 32 & 43.14 & 8.92 & 330 \\ UpCSPN-7 & AdaDRBins & 32 & 43.57 & 9.26 & 310 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Computational analyses of the proposed networks with different structures where MAC expresses the number of multiply accumulated operations. In this study, we follow the rule of gradually increasing the stacked ingredients and layers to develop our networks. The content of Table 6 can imply that the model with the SimpleUP + AdaBins decoder could be a teacher network to distill the model with the solo-SimpleUp decoder. Similarly, the model with the UpCSPN-\(l\) + AdaDRBins decoder can play a teacher role for the model using the UpCSPN-\(m\) + AdaDRBins decoder for \(l>m\). Hence, based on this design strategy, the teacher-and-student structure can be easily attained by arbitrarily reducing the layers of a complex model for distillation. Observing Tables 5, 6 and 8, the proposed model using MobileNet_s, SimpleUP and 32-bin AdaDRBins could be the best selection in real-time low-cost MR systems in terms of the efficiency-accuracy leverage. Because the depths captured by the ToF depth camera in MR glasses are overly blurry, the original depths near fingers are mostly lost during hand-moving actions.