Investigating a Policy-Based Formulation for Endoscopic Camera Pose Recovery

2026-03-20

AI Summary

This work addresses the challenge of camera pose estimation in endoscopic navigation, where traditional geometry-based methods often fail due to unreliable feature matching under low-texture conditions or abrupt illumination changes commonly encountered in surgical environments. To overcome this limitation, the study introduces, for the first time, a strategy-learning approach to endoscopic visual navigation by proposing an end-to-end motion prediction framework. This method directly predicts short-term relative camera poses conditioned on historical camera states through behavioral cloning of expert demonstrations, thereby circumventing explicit geometric modeling, feature matching, or 3D reconstruction. Experimental results on cadaveric sinus endoscopy datasets demonstrate that the proposed approach achieves the lowest translational error and competitive rotational accuracy, while exhibiting superior robustness in low-texture scenarios compared to conventional techniques.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In endoscopic surgery, surgeons continuously locate the endoscopic view relative to the anatomy by interpreting the evolving visual appearance of the intraoperative scene in the context of their prior knowledge. Vision-based navigation systems seek to replicate this capability by recovering camera pose directly from endoscopic video, but most approaches do not embody the same principles of reasoning about new frames that makes surgeons successful. Instead, they remain grounded in feature matching and geometric optimization over keyframes, an approach that has been shown to degrade under the challenging conditions of endoscopic imaging like low texture and rapid illumination changes. Here, we pursue an alternative approach and investigate a policy-based formulation of endoscopic camera pose recovery that seeks to imitate experts in estimating trajectories conditioned on the previous camera state. Our approach directly predicts short-horizon relative motions without maintaining an explicit geometric representation at inference time. It thus addresses, by design, some of the notorious challenges of geometry-based approaches, such as brittle correspondence matching, instability in texture-sparse regions, and limited pose coverage due to reconstruction failure. We evaluate the proposed formulation on cadaveric sinus endoscopy. Under oracle state conditioning, we compare short-horizon motion prediction quality to geometric baselines achieving lowest mean translation error and competitive rotational accuracy. We analyze robustness by grouping prediction windows according to texture richness and illumination change indicating reduced sensitivity to low-texture conditions. These findings suggest that a learned motion policy offers a viable alternative formulation for endoscopic camera pose recovery.

Problem

• endoscopic camera pose recovery
• low texture
• illumination changes
• vision-based navigation
• geometric optimization

Innovation

• policy-based pose estimation
• endoscopic navigation
• learned motion policy
• geometry-free tracking
• visual odometry