## 4 Experiments
### Qualitative Analysis of DI-MDE Performance in Dynamic Scenarios  
While the quantitative results demonstrate DI-MDE's superiority over existing methods, a more detailed analysis is needed to fully support the claim that DI-MDE solves the error bias problem in dynamic scenarios. To illustrate this, we provide an in-depth comparison of DI-MDE performance with multiple moving objects and scenes with varying levels of motion complexity. The following tables summarize DI-MDE's effectiveness in these challenging environments. One of the key advantages of DI-MDE is its ability to maintain accurate depth predictions in scenes with multiple moving objects. Table 6 highlights the comparative performance of DI-MDE against baseline models in a set of dynamic scenarios featuring different numbers of moving targets. As shown in Table 6, DI-MDE consistently outperforms AdaBins and FC-CRFs in scenes with varying numbers of moving targets. The iterative refinement process employed by DI-MDE allows for more precise depth predictions, especially in scenarios where baseline methods struggle to handle depth discontinuities and the motion of multiple objects. In addition to multiple moving objects, DI-MDE demonstrates an enhanced performance in scenarios where the motion complexity increases. Table 7 compares the performance of DI-MDE and existing models in scenes with different levels of motion complexity, measured by the number of independently moving objects and their speed. Table 7 illustrates how the DI-MDE iterative refinement process enables the model to better handle complex motion scenarios. As the speed and number of moving targets increase, DI-MDE shows significant reductions in absolute relative error and RMSE, while maintaining a high threshold accuracy. To further explore DI-MDE's ability to solve the error bias problem, Table 8 compares the depth estimation error across multiple independent objects of varying sizes and speeds in complex dynamic scenes. These results demonstrate how DI-MDE mitigates the error bias typically seen in baseline models when handling fast-moving or overlapping objects. Table 8 further illustrates how DI-MDE handles varying object types and speeds, particularly in terms of mitigating the error bias. The results clearly show that DI-MDE reduces the error bias significantly across different object types and speeds, especially when compared to AdaBins and FC-CR. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Scenario** & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Absolute Relative Error (Abs Rel)**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Threshold Accuracy \(\mathbf{\delta<1.25\) (\%)**}} \\ \cline{3-4}
**(Dynamic Targets)** & & & & \\ \hline \multirow{3}{*}{3 Moving Targets} & AdaBins [21] & 0.115 & 5.300 & 85.2 \\ \cline{2-4}  & FC-CRFs [22] & 0.109 & 5.200 & 86.1 \\ \cline{2-4}  & DI-MDE (Ours) & 0.097 & 4.300 & 87.5 \\ \hline \multirow{3}{*}{5 Moving Targets} & AdaBins [21] & 0.120 & 5.750 & 83.5 \\ \cline{2-4}  & FC-CRFs [22] & 0.114 & 5.600 & 84.4 \\ \cline{2-4}  & DI-MDE (Ours) & 0.102 & 4.750 & 86.3 \\ \hline \multirow{3}{*}{10 Moving Targets} & AdaBins [21] & 0.133 & 6.000 & 80.2 \\ \cline{2-4}  & FC-CRFs [22] & 0.128 & 5.950 & 81.4 \\ \cline{1-1} \cline{2-4}  & DI-MDE (Ours) & 0.108 & 5.000 & 84.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Illustration moving objects results. In terms of floating-point operations per second (FLOPs), DI-MDE requires 124.7 GFLOPs per forward pass, which is approximately 18% lower than AdaBins and FC-CRFs. AdaBins, for instance, consumes around 153.2 GFLOPs, while FC-CRFs require 160.4 GFLOPs. This reduction in FLOPs demonstrates that DI-MDE is more computationally efficient, allowing for faster inference times and lower energy consumption in real-time applications. The lower computational demand also makes DI-MDE more scalable, particularly in environments where processing power is limited. Running time is another key metric, especially for models designed for real-time deployment.