## 6 Conclusions  
In this paper, we introduced the DI-MDE framework, a novel approach to monocular depth estimation that addresses the critical challenges of depth prediction in dynamic scenes, specifically the issues of scale ambiguity and depth refinement. Our approach integrates two key innovations: the DSA module and the iterative elastic bin refinement process. Together, these components enable our model to produce more accurate and consistent depth predictions, particularly in regions where existing methods struggle, such as scenes with independently moving objects. The DSA module resolves the long-standing problem of scale ambiguity by aligning the depth scales between static and dynamic regions, ensuring that moving objects have depth predictions that are consistent with the static background. Meanwhile, iterative elastic bin refinement dynamically adjusts depth predictions across multiple stages, focusing on regions with high uncertainty and improving depth granularity. We evaluated DI-MDE on multiple benchmark datasets, including KITTI, SUN RGB-D, and NYU Depth v2, where our method consistently outperformed SOTA models in terms of both absolute relative error and root mean squared error. Our model demonstrated superior depth prediction accuracy in both static and dynamic scenes, as well as competitive computational efficiency, making it suitable for real-time applications. The success of DI-MDE in these experiments highlights the importance of addressing both depth refinement and scale alignment, especially in dynamic environments. By improving depth accuracy in moving objects and refining depth predictions iteratively, our framework sets a new standard for self-supervised monocular depth estimation. The proposed DI-MDE framework offers several advantages, especially in handling dynamic scenes with moving objects, but like any model, it has limitations that should be addressed to provide a balanced view. One of the key limitations involves its performance in low-light or poor ambient lighting conditions. Monocular depth estimation relies heavily on the visual cues from the scene, such as texture, color contrast, and edges. In scenarios with poor ambient light, these visual cues can be significantly diminished, leading to less accurate depth predictions. In low-light conditions, the lack of sufficient illumination may cause the model to struggle with differentiating between objects and the background. The reduced visibility of edges and depth discontinuities can lead to higher uncertainty in depth predictions, particularly in dynamic scenes where motion further complicates depth estimation. While DI-MDE's iterative refinement process and scale alignment can mitigate some of these challenges by focusing on areas of high uncertainty, its effectiveness is reduced when the visual information in the scene is degraded due to poor lighting. The DSA module, in particular, relies on motion cues, which are harder to detect in dark environments, limiting its ability to align depth predictions correctly for moving objects. In future work demonstrating these challenges and the overall effectiveness of DI-MDE, more visual results could be provided, particularly showing the model performance in a variety of lighting conditions, including poor ambient light. Visual comparisons between DI-MDE and other state-of-the-art methods in low-light scenarios would provide clearer evidence of the model's limitations and strengths. Future work will explore further enhancements, such as handling extreme occlusions in dynamic regions and improving generalization to more complex real-world scenes. Additionally, we aim to extend the DI-MDE framework to other applications, including 3D scene reconstruction and autonomous navigation, where accurate depth estimation plays a crucial role. Our proposed DI-MDE framework significantly advances the field of monocular depth estimation, providing a robust and scalable solution to the challenges posed by dynamic scenes, and opening new avenues for research and application in 3D vision.