## 4 Experiments
### Quantitative Results
This decline can be attributed to the critical role the IEBins module plays in the iterative refinement of depth predictions. Without this component, the model is forced to rely on a single-stage depth prediction, which leads to coarser and less precise depth estimates. In static scenes, the absence of iterative refinement means that the model cannot adjust its depth predictions based on the uncertainty of depth estimates. Static regions, which often exhibit smooth depth transitions, particularly benefit from the IEBins module's ability to progressively narrow down depth predictions, ensuring finer granularity in areas with low-depth variation. In these regions, the IEBins module ensures that even subtle depth changes are captured accurately. The ablation results reflect this, as removing the IEBins module reduces the overall accuracy in static scenes, where the lack of iterative refinement leads to more uniform and less precise depth maps. The variant without DSA also shows a clear reduction in accuracy, particularly in dynamic scenes. The DSA module is designed to resolve the scale ambiguity that arises when moving objects are present. By aligning the depth predictions for dynamic objects with the static background, the DSA module ensures that the entire scene maintains consistent depth estimates. When the DSA module is removed, depth predictions for moving objects tend to diverge from the static background, leading to scale inconsistency. This misalignment is particularly problematic in scenes with significant object motion, where the relative depth between moving objects and static regions becomes skewed. As a result, the model produces more biased depth predictions in dynamic regions, increasing the absolute relative error and RMSE values. The ablation results clearly show this increase in error, indicating the essential role of the DSA module in maintaining scale consistency and preventing biased in-depth predictions for dynamic objects. The full DI-MDE framework integrates both the IEBins and DSA modules, and their combined effect is critical in improving the model's overall performance. The iterative refinement of depth estimates provided by the IEBins module complements the scale alignment enforced by the DSA module, ensuring that depth predictions are both precise and consistent across the entire scene. The ablation study shows that removing either module leads to a significant drop in performance, but it also highlights their interdependence. While the IEBins module focuses on refining depth estimates iteratively, the DSA module ensures that these refinements are correctly aligned in scale. Together, these modules enable DI-MDE to achieve superior accuracy in both static and dynamic scenes, as demonstrated by the full model results in Table 2.  
In this section, we compare our DI-MDE framework with the current state-of-the-art (SOTA) monocular depth estimation models. These comparisons focus on both the accuracy of depth predictions, particularly in dynamic regions, and the computational efficiency of the models. We highlight key differences in methodology and present quantitative comparisons across several benchmark datasets, including KITTI, SUN RGB-D, and NYU Depth v2. We compare our DI-MDE framework against the following prominent SOTA models: AdaBins [21] introduced the concept of adaptive bins for depth estimation, where depth predictions are discretized into bins of varying sizes based on scene complexity. This allows for improved handling of large depth variations within the same image. FC-CRFs [22] leverages neural window-based conditional random fields (CRFs) to refine depth predictions. This method uses CRFs to model the spatial dependencies in depth maps,improving prediction accuracy in regions with ambiguous depth cues. IEBins [7] builds on classification-regression-based methods and introduces iterative elastic bins for depth estimation. The elastic bins dynamically adjust based on uncertainty, allowing for iterative refinement of depth predictions [6].