To improve monocular depth and pose estimation in gastrointestinal endoscopy using a self-supervised framework that integrates luminance and edge cues, enhancing the reliability of navigation and detection.
Key Findings:
PRISM achieves state-of-the-art depth estimation and comparable pose accuracy on phantom data, demonstrating its effectiveness over traditional methods.
Training on real-world data improves generalization compared to synthetic or phantom data, highlighting the importance of diverse training sets.
Weak supervision through edge-guided loss enhances pose estimation without degrading depth accuracy, suggesting a novel approach to training in challenging environments.
Interpretation:
The integration of structural and photometric cues significantly stabilizes depth and pose estimation in challenging endoscopic environments, effectively addressing issues of illumination variability and low-texture surfaces, which are common in clinical settings.
Limitations:
The reliance on synthetic and phantom datasets may limit the generalizability of the model; future work should explore more diverse real-world datasets.
The performance may vary across different datasets and models due to optimal temporal sampling differences, indicating a need for adaptive sampling strategies.
Conclusion:
PRISM demonstrates improved robustness and accuracy in depth and pose estimation for endoscopic applications, emphasizing the critical role of integrating multiple cues for enhanced performance in real-world scenarios.
