Bridging the Ex-Vivo to In-Vivo Gap: Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913046772842496 |
|---|---|
| author | Aich, Ankan Ryan, Emma D. Moe, Kris Schmale, Isaac Man, Li-Xing Lee, Yangming |
| author_facet | Aich, Ankan Ryan, Emma D. Moe, Kris Schmale, Isaac Man, Li-Xing Lee, Yangming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging the Ex-Vivo to In-Vivo Gap: Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments Aich, Ankan Ryan, Emma D. Moe, Kris Schmale, Isaac Man, Li-Xing Lee, Yangming Computer Vision and Pattern Recognition Robotics 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. |
| title | Bridging the Ex-Vivo to In-Vivo Gap: Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2512.23786 |