## 4 Experiments
### Quantitative Results
Furthermore, the IEBins process allows for finer granularity in depth estimation. Unlike other models that make a one-time prediction, DI-MDE iteratively improves depth accuracy by refining the depth bins at each stage. This granularity becomes essential in dynamic scenes, where the motion of objects introduces significant variations in depth. The ability to progressively refine the depth map ensures that even small details and complex regions are captured accurately, which is reflected in the lower Abs Rel and RMSE values reported in Table 3.  
In addition to refining depth estimates, the IEBins module plays a crucial role in resolving depth discontinuities, which are particularly challenging in dynamic scenes. Depth discontinuities occur at the boundaries of objects where there are sudden changes in depth, and these are often exacerbated when objects are moving. In such scenarios, models that do not refine their depth predictions iteratively tend to produce coarse estimates, resulting in inaccurate depth boundaries. The DI-MDE framework, by iterating over depth predictions and dynamically adjusting the bin widths based on uncertainty, provides sharper and more accurate depth boundaries, reducing errors in these critical regions. This is why DI-MDE outperforms other SOTA methods in dynamic scenes, where depth discontinuities are most pronounced. Another factor contributing to the improved performance of DI-MDE is the way it handles the scale ambiguity inherent in dynamic scenes. The DSA module in DI-MDE ensures that the depth predictions for moving objects are consistent with the static background. This alignment, combined with the iterative refinement of depth predictions through IEBins, ensures that the entire depth map, both for static and dynamic regions, is consistent and accurate. In contrast, other models often struggle to maintain this consistency because they lack a mechanism for iterative refinement or scale alignment. While models such as AdaBins and FC-CRFs perform well in static scenes, their lack of iterative refinement limits their accuracy in dynamic regions where depth estimates need to be adjusted over time. We introduce the \(p\)-value metric in Table 4 to evaluate the statistical significance of differences between our method and other SOTA models. The \(p\)-value is a widely used statistical metric that quantifies the probability of observing the results given that the null hypothesis is true. A smaller \(p\)-value indicates stronger evidence against the null hypothesis, suggesting that the observed differences are statistically significant. For this analysis, we conducted pairwise \(t\)-tests between DI-MDE and each baseline method across evaluation metrics. The reported \(p\)-values demonstrate the significance of the performance improvements offered by DI-MDE. A detailed explanation of the statistical test methodology can be found in [24]. Despite our model iterative nature, the DI-MDE framework achieves computational efficiency through several design optimizations. Each iteration of the IEBins module operates on a progressively narrowed search space, reducing the computational load at later stages. The dynamic adjustment of bin widths ensures that computational resources are concentrated on regions with high depth uncertainty, minimizing redundant calculations in low-uncertainty areas Figure 3. Additionally, the DSA module is computationally lightweight, as it applies scalar adjustments to depth predictions without recalculating them from scratch. Compared to non-iterative methods like AdaBins, which require large-scale discretization of the depth range, the DI-MDE framework dynamically balances computational effort and predictive accuracy. This approach allows the framework to maintain competitive inference times, as shown in Table 4, while delivering state-of-the-art performance in terms of accuracy and consistency. In addition to numerical results, we compare the visual quality of depth maps produced by each model, focusing on complex dynamic scenes with large depth variations.