## 4 Experiments
### Quantitative Results
Our DI-MDE framework generates depth maps that are more consistent and visually accurate, especially in regions with moving objects, where scale ambiguity often causes other models to struggle. AdaBins [21], while effective in static regions, sometimes produces artifacts in dynamic regions, particularly where depth boundaries are unclear. The FC-CRFs [22] model excels in maintaining spatial coherence but occasionally introduces over-smoothing, especially near object boundaries. The IEBins [7] iterative elastic bin refinement process helps to produce sharper depth boundaries, but it struggles with extreme depth variations in dynamic scenes Figure 4. By integrating the DSA module, our model effectively handles dynamic regions, maintaining consistency in both static and moving objects. We also compare the inference time and model complexity of each method to highlight the practical advantages of our approach. Despite achieving superior accuracy, our DI-MDE framework maintains competitive efficiency, demonstrating that it is suitable for real-time applications (Table 5). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Abs Rel (Mean \(\pm\) Std)** & **RMSE (Mean \(\pm\) Std)** & \(p\)**-Value (Significance)** \\ \hline AdaBins & 0.103 \(\pm\) 0.002 & 4.580 \(\pm\) 0.020 & \textless{}0.05 \\ \hline FC-CRFs & 0.095 \(\pm\) 0.003 & 4.300 \(\pm\) 0.030 & \textless{}0.05 \\ \hline DI-MDE & 0.087 \(\pm\) 0.001 & 4.011 \(\pm\) 0.010 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical significance and consistency across methods. Figure 3: Displays visual results comparing depth maps generated by the DI-MDE framework and other baseline methods on the SUN RGB-D dataset. This figure emphasizes the ability of DI-MDE to handle dynamic scenes more effectively, particularly in regions with moving objects. Our method, while having slightly more parameters than IEBins, offers significantly faster inference times, making it more feasible for real-time deployment in depth-sensitive applications such as autonomous driving or augmented reality. Compared to SOTA models, our DI-MDE framework offers several key advantages. First, the DSA module effectively addresses the issue of scale ambiguity in dynamic regions, ensuring depth consistency across the entire scene. Additionally, the iterative elastic bin refinement process enhances the precision of depth predictions, particularly in regions where uncertainty is high, by progressively refining predictions over multiple stages. Lastly, despite these additional refinement mechanisms, our model maintains computational efficiency, offering competitive inference times suitable for real-time applications. However, like other SOTA models, our approach has limitations. While the DSA module effectively handles scale ambiguity, extreme occlusions in dynamic scenes can still present challenges, which we aim to address in future work. Our DI-MDE framework outperforms existing SOTA models in both static and dynamic depth estimation tasks, offering a well-rounded solution that balances accuracy, efficiency, and generalizability. The DI-MDE framework demonstrates significant improvements in resolving scale ambiguities for moving objects, particularly near-depth discontinuity boundaries. Quantitative evaluations using the boundary RMSE (B-RMSE) and boundary absolute relative error (B-Abs Rel) metrics show that the framework reduces boundary errors by approximately 15% compared to AdaBins and 12% compared to FC-CRFs, highlighting its effectiveness in dynamic regions. Importantly, these improvements are achieved without compromising performance in static regions, ensuring consistent accuracy across the entire scene. To achieve accurate scale alignment without introducing artifacts, the DSA module applies scale adjustments selectively to dynamic regions identified using motion masks derived from optical flow. A smoothness regularization term is included to enforce spatial consistency while preserving depth discontinuities at object boundaries.