## 5 Discussion  
The proposed methodology demonstrates several strengths compared to existing approaches. The IEBins module significantly enhances depth prediction accuracy by pro \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Scene Complexity (Objects and Speed)** & **Method** & **Abs Rel** & **RMSE** &
\begin{tabular}{l} **Threshold Accuracy** \\ **\(\mathbf{\delta<1.25\) (\%)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{Low (2 slow-moving targets)} & AdaBins [21] & 0.108 & 4.900 & 86.7 \\ \cline{2-5}  & FC-CRFs [22] & 0.103 & 4.850 & 87.2 \\ \cline{2-5}  & DI-MDE (Ours) & 0.090 & 4.100 & 89.5 \\ \hline \multirow{3}{*}{Medium (5 moderate-speed targets)} & AdaBins [21] & 0.120 & 5.400 & 84.3 \\ \cline{2-5}  & FC-CRFs [22] & 0.115 & 5.350 & 85.0 \\ \cline{2-5}  & DI-MDE (Ours) & 0.104 & 4.600 & 86.8 \\ \hline \multirow{3}{*}{High (10 fast-moving targets)} & AdaBins [21] & 0.138 & 6.100 & 79.0 \\ \cline{2-5}  & FC-CRFs [22] & 0.134 & 6.000 & 79.8 \\ \cline{2-5}  & DI-MDE (Ours) & 0.110 & 5.200 & 82.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Moving scenario comparison. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Object Type (Size and Speed)** & **Method** & **Abs Rel** & **RMSE** & **Error Bias (\%)** \\ \hline \multirow{3}{*}{Large, slow-moving (Vehicle)} & AdaBins [21] & 0.110 & 5.100 & 5.8 \\ \cline{2-5}  & FC-CRFs [22] & 0.105 & 5.000 & 5.1 \\ \cline{2-5}  & DI-MDE (Ours) & 0.094 & 4.400 & 3.7 \\ \hline \multirow{3}{*}{Small, fast-moving (Pedestrian)} & AdaBins [21] & 0.125 & 5.500 & 7.4 \\ \cline{2-5}  & FC-CRFs [22] & 0.119 & 5.450 & 7.1 \\ \cline{2-5}  & DI-MDE (Ours) & 0.108 & 4.700 & 5.2 \\ \hline \multirow{3}{*}{Medium, moderate speed (Animal)} & AdaBins [21] & 0.118 & 5.200 & 6.5 \\ \cline{2-5}  & FC-CRFs [22] & 0.113 & 5.150 & 6.2 \\ \cline{1-1} \cline{2-5}  & DI-MDE (Ours) & 0.101 & 4.600 & 4.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Varying object types and speeds handing. gressively refining depth estimates over multiple iterations. This dynamic adjustment of bin widths allows the model to focus computational resources on areas with high-depth uncertainty, achieving superior performance in scenarios with large depth variations. Unlike single-stage prediction methods such as AdaBins and FC-CRFs, this iterative approach provides finer granularity and improved handling of depth discontinuities. Another key innovation is the DSA module, which effectively addresses the challenge of scale ambiguity. In dynamic scenes, moving objects often disrupt depth consistency due to their independent motion. The DSA module aligns the depth scales of dynamic objects with the static background, ensuring consistent and accurate depth predictions across the scene. This capability distinguishes our approach from existing methods, such as Mining Supervision, which lack integrated scale-alignment mechanisms. Furthermore, the self-supervised training framework eliminates the need for expensive ground truth labels, leveraging monocular video sequences for training. This makes the proposed methodology highly scalable and practical for real-world applications where labeled data are scarce. Despite these sophisticated mechanisms, the framework maintains computational efficiency. It achieves faster inference times compared to computationally intensive methods like FC-CRFs, making it suitable for real-time applications such as autonomous driving and robotics. However, the methodology also has limitations. Performance can be affected in low-light conditions, where the reliance on visual cues, such as texture and edges, may result in less accurate depth predictions. While the iterative refinement process mitigates some challenges, degraded visual inputs in poor lighting environments inherently limit the model effectiveness. Similarly, in scenes with significant occlusions, the DSA module may struggle to accurately align depth scales when large parts of dynamic objects are not visible, leading to residual errors in depth predictions.