Surgical-DINO: adapter learning of foundation models for depth estimation in endoscopic surgery

Beilei Cui, Mobarakol Islam, Long Bai, Hongliang Ren

Abstract: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g., DINOv2), recent works observed its limitations in medical and surgical domain-specific applications. This work presents a low-ranked adaptation (LoRA) of the foundation model for surgical depth estimation.