Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
arXiv:2606.17340v1 Announce Type: cross Abstract: Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substantial appearance variation across domains, all of which complicate pose estimation, depth prediction, and image-to-anatomy alignment. Although recent vision foundation models have shown promise, their learned representations often remain insufficiently geometry-consistent, hindering stable feature correspondence and limiting their reliability for downstream navigation tasks. We propose a unified f
The rapid advancement of vision foundation models is creating new opportunities for their application in specialized, high-stakes fields like surgery, where geometry consistency is paramount.
This development addresses a critical challenge in medical robotics and image-guided surgery, potentially enabling more precise, autonomous, and safer procedures via AI.
The ability to adapt foundation models for geometry-consistent representations significantly enhances the reliability of AI for depth perception and navigation in complex, previously challenging environments like endoscopic surgery.
- · Medical robotics companies
- · Surgical AI developers
- · Patients undergoing surgery
- · Hospitals and healthcare systems
- · Traditional navigation systems
- · Companies reliant on purely human visual interpretation for complex surgical tas
Improved accuracy and autonomy in robotic-assisted surgeries.
Reduced surgical errors and accelerated adoption of AI in operating rooms, especially for minimally invasive procedures.
Potential for fully autonomous surgical procedures in highly controlled environments, fundamentally altering the role of human surgeons.
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Read at arXiv cs.AI