Self-supervised monocular depth estimation has emerged as a major research focus in recent years, primarily due to the elimination of ground-truth depth dependence. However, the prevailing architectures in this domain suffer from inherent limitations: existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions. These assumptions are often violated in real-world scenarios due to dynamic objects, non-Lambertian reflectance, and unstructured background elements, leading to pervasive artifacts such as depth discontinuities (“holes”), structural collapse, and ambiguous reconstruction. To address these challenges, we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network, enhancing its ability to model complex scene dynamics. Our contributions are threefold: (1) a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations; (2) a physically-informed loss function that couples dynamic pose and depth predictions, designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles; (3) an efficient SE (3) transformation parameterization that streamlines network complexity and temporal pre-processing. Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity, significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.