用合成先验提升手术场景单目深度估计，解决真实手术中反光导致的误差问题。

Accurate Monocular Depth Estimation (MDE) is critical for autonomous robotic surgery. However, existing self-supervised methods often exhibit a severe "ex-vivo to in-vivo gap": they achieve high accuracy on public datasets but struggle in actual clinical deployments. This disparity arises because the severe specular reflections and fluid-filled deformations inherent to real surgeries. Models trained on noisy real-world pseudo-labels consequently suffer from severe boundary collapse. To address this, we leverage the high-fidelity synthetic priors of the \textit{Depth Anything V2} architecture, which inherently capture precise geometric details, and efficiently adapt them to the medical domain using Dynamic Vector Low-Rank Adaptation (DV-LORA). Our contributions are two-fold. Technically, our approach establishes a new state-of-the-art on the public SCARED dataset; under a novel physically-stratified evaluation protocol, it reduces Squared Relative Error by over 17\% in high-specularity regimes compared to strong baselines. Furthermore, to provide a rigorous reality check for the field, we introduce \textbf{ROCAL-T 90} (Real Operative CT-Aligned Laparoscopic Trajectories 90), the first real-surgery validation dataset featuring 90 clinical endoscopic sequences with sub-millimeter ($< 1$mm) ground-truth trajectories. Evaluations on ROCAL-T 90 demonstrate our model's superior robustness in true clinical settings.