## 4 Experiments
### Quantitative Results
This method proposes a novel scale-alignment network to decouple depth estimation for static and dynamic regions. By addressing scale ambiguity in moving objects, this model improves depth prediction accuracy, particularly in dynamic scenes. ECoDepth [23] introduces diffusion-based conditioning to improve monocular depth estimation. This approach conditions depth predictions on a latent diffusion model, leading to improved depth accuracy by leveraging both global and local image features Figure 2.  
We report the performance of our DI-MDE framework and the aforementioned SOTA models across key monocular depth estimation benchmarks (Table 3). The evaluation metrics include Abs Rel, RMSE, and threshold accuracy (\(\delta<1.25\)). As shown in the table, our DI-MDE model consistently outperforms previous methods in terms of absolute relative error and RMSE, indicating superior depth accuracy. Notably, DI-MDE achieves the best results on the SUN RGB-D dataset, surpassing FC-CRFs [22] and IEBins [7]. Our iterative refinement process and the DSA module provide significant improvements in dynamic scenes, which is evident in the performance boost in the \(\delta<1.25\) accuracy metric. In the Comparative Experiments Section, particularly in Table 3, the DI-MDE framework shows significant improvement over SOTA monocular depth estimation methods. While performance metrics such as Abs Rel and RMSE reflect this superiority, a deeper exploration is necessary to explain why the DI-MDE framework, specifically the IEBins refinement process, delivers such noticeable performance improvements, particu \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Abs Rel \(\downarrow\)** & **RMSE \(\downarrow\)** & \(\delta<1.25\uparrow\) & **Dataset** \\ \hline AdaBins [21] & 0.103 & 4.580 & 0.961 & KITTI \\ \hline FC-CRFs [22] & 0.095 & 4.300 & 0.974 & KITTI \\ \hline IEBins [7] & 0.090 & 4.250 & 0.973 & KITTI \\ \hline Mining Supervision [6] & 0.108 & 4.710 & 0.940 & SUN RGB-D \\ \hline ECoDepth [23] & 0.092 & 4.100 & 0.970 & NYU Depth v2 \\ \hline \multirow{3}{*}{DI-MDE (Ours)} & 0.087 & 4.011 & 0.978 & KITTI \\ \cline{1-1} \cline{2-5}  & 0.097 & 4.300 & 0.981 & SUN RGB-D \\ \cline{1-1} \cline{2-5}  & 0.097 & 4.300 & 0.980 & NYU Depth v2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of SOTA models across key monocular depth estimation benchmarks. Figure 2: Performance metrics for various depth estimation models, highlighting the performance of the DI-MDE framework on the NYU Depth v2 dataset. larly in dynamic scenarios. One of the key differences between DI-MDE and other models lies in the approach to depth refinement. Existing SOTA methods often rely on single-stage depth prediction. In these models, depth predictions are generated in a single forward pass, leaving no opportunity for further refinement. This limitation becomes particularly apparent in dynamic scenes, where moving objects introduce additional complexity due to scale ambiguity and depth discontinuities. The lack of iterative refinement means that these models cannot adjust their predictions based on uncertainty or correct initial inaccuracies, especially in areas with significant depth variation. The DI-MDE framework, by contrast, introduces the IEBins module, which refines depth predictions iteratively over multiple stages. This process enables the network to progressively improve its predictions, especially in regions with high depth uncertainty. In dynamic scenes, this iterative approach is particularly valuable. The IEBins module dynamically adjusts bin widths based on uncertainty estimates, which allows the model to focus more effectively on areas where depth predictions are initially less accurate. This refinement is especially useful for handling depth discontinuities, such as the boundaries of moving objects, where single-stage models typically struggle. By continuously narrowing down the search space for depth predictions, the iterative refinement process results in more precise and reliable depth estimates in dynamic regions.