DUNEDepth: Bronchoscopic Depth and Pose Estimation via Hybrid Adaptation of a 3D-Vision Foundation Model

Abstract

State-of-the-art bronchoscopy guidance systems and computer-aided diagnosis methods have emerged as important tools for early lung cancer detection. Effective use of these tools depends on being able to accurately link a patient's bronchoscopic airway exam video to their pre-operative 3D chest CT scan. Unfortunately, the derivation of such links, which include bronchoscope depth and camera motion, from the exam video remains an open problem. Building on recently proposed 3D-vision foundation models, we propose DUNEDepth, a hybrid architecture that combines a shared DUNE ViT encoder, a twin MASt3R cross-attention decoder structure, and a prediction head that includes elements of the MASt3R and Reloc3r models. To fine-tune the architecture to our application, we utilize an integrated hybrid synthetic-supervised/self-supervised training strategy that draws on both bronchoscopic video and CT-derived virtual bronchoscopic views. Experiments drawing on bronchoscopic video sequences demonstrate that DUNEDepth more accurately derives depth information and camera motion trajectories than previously proposed self-supervised methods.