## 4 Experiments
### Quantitative Results  
We first present the quantitative results of our method compared to existing state-of-the-art models, focusing on the key metrics mentioned above. In this section, we evaluate the performance of our proposed DI-MDE framework against current state-of-the-art (SOTA) methods in monocular depth estimation. We compared the proposed method with SOTA methods such as AdaBins, FC-CRFs, and IEBins, which we use as baselines for comparison in this study. Table 1 summarizes the results of our process and other SOTA models on the SUN RGB-D dataset, highlighting the superiority of DI-MDE in handling dynamic scenes. As shown in the table, our DI-MDE framework consistently outperforms existing methods across all key metrics. The improvement is most significant in the Abs Rel and RMSE, which are key indicators of the overall accuracy of depth predictions. Additionally, the threshold accuracy at \(\delta_{1}\), \(\delta_{2}\), and \(\delta_{3}\) also shows notable improvement, particularly at lower thresholds, indicating better accuracy even in challenging regions with high depth variation. To further validate the effectiveness of DI-MDE, we present qualitative results that demonstrate the improvements in depth predictions, particularly in challenging dynamic scenes. Our results show examples of depth maps predicted by our model compared to baseline methods, highlighting how our framework better handles scale ambiguity in dynamic scenes. The visual improvements demonstrate that DI-MDE not only improves numerical accuracy but also significantly enhances the visual quality of the predicted depth maps, especially in dynamic and complex environments. To better understand the contribution of individual components of the DI-MDE framework, we perform ablation studies by progressively removing or modifying key components. The results of these ablation experiments are shown in Table 2.  
The ablation experiments in Table 2 illustrate the impact of removing key components of the DI-MDE framework, particularly the IEBins refinement and the DSA module. While \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Abs Rel \(\downarrow\)** & **RMSE \(\downarrow\)** & \(\mathbf{\delta_{1}\uparrow}\) & \(\mathbf{\delta_{2}\uparrow}\) & \(\mathbf{\delta_{3}\uparrow}\) \\ \hline FC-CRFs [21] & 0.112 & 4.960 & 0.845 & 0.950 & 0.988 \\ \hline AdaBins [22] & 0.103 & 4.580 & 0.859 & 0.961 & 0.993 \\ \hline ECoDepth [23] & 0.107 & 4.620 & 0.852 & 0.955 & 0.991 \\ \hline DI-MDE (Ours) & 0.097 & 4.300 & 0.870 & 0.967 & 0.995 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of SOTA methods on the SUN RGB-D dataset. This dataset was selected for its variety of challenging indoor scenes with dynamic elements. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model Variant** & **Abs Rel \(\downarrow\)** & **RMSE \(\downarrow\)** & \(\mathbf{\delta_{1}\uparrow}\) & \(\mathbf{\delta_{2}\uparrow}\) & \(\mathbf{\delta_{3}\uparrow}\) \\ \hline Full DI-MDE & 0.097 & 4.300 & 0.870 & 0.967 & 0.995 \\ \hline Without Iterative Elastic & & & & & \\ Bin Refinement & 0.105 & 4.720 & 0.855 & 0.960 & 0.991 \\ \hline Without DSA & 0.108 & 4.750 & 0.850 & 0.958 & 0.990 \\ \hline Without Both & & & & & \\ Components & 0.112 & 4.960 & 0.845 & 0.950 & 0.988 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study results of the DI-MDE framework on the NYU Depth v2 dataset. NYU Depth v2 is a standard benchmark for evaluating contributions of individual components in depth estimation methods. these results provide clear numerical evidence of the contribution of each module, it is essential to offer a more in-depth discussion of their specific roles in improving accuracy, especially in differentiating performance across static and dynamic scenes. The variant without iterative elastic bin refinement demonstrates a noticeable decline in performance across all metrics, with an increase in Abs Rel and RMSE and a reduction in threshold accuracy.