Stereo Endoscopic Camera Pose Optimal Estimation by Structure Similarity Index Measure Integration

• Published in: The international journal of medical robotics + computer assisted surgery Vol. 21; no. 3; pp. e70078 - n/a
• Main Authors: Lian, Ruoqi, Li, Wei, Hao, Junchen, Zhang, Yanfang, Jia, Fucang
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.06.2025
• Subjects: Ablation; Algorithms; camera pose estimation; Cameras; Endoscopy - methods; Error reduction; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Navigation systems; Optical flow (image analysis); Pose estimation; Reproducibility of Results; Robotic Surgical Procedures - methods; Root-mean-square errors; stereo endoscope; structural similarity index measure; Surgery, Computer-Assisted - methods; surgical navigation; structural similarity index measure; surgical navigation; camera pose estimation; stereo endoscope
• ISSN: 1478-5951; 1478-596X; 1478-596X
• DOI: 10.1002/rcs.70078

ABSTRACT Background Accurate endoscopic camera pose estimation is crucial for real‐time AR navigation systems. While current methods primarily use depth and optical flow, they often ignore structural inconsistencies between images. Methods Leveraging the RAFT framework, we process sequential stereo RGB pairs to extract optical flow and depth features for pose estimation. To address structural inconsistencies, we refine the weights for both 2D and 3D residuals by computing SSIM indices for the left and right views, as well as pre‐ and post‐optical flow transformations. The SSIM metric is also used in the loss function. Results Experiments on the StereoMIS dataset demonstrate our method's improved pose estimation accuracy compared to rigid SLAM methods, showing a lower accumulated trajectory error (ATE‐RMSE: 18.5 mm). Additionally, ablation experiments achieved an 11.49% reduction in average error. Conclusion The pose estimation accuracy has been improved by incorporating SSIM. The code is available at: https://github.com/lianrq/pose‐estimation‐by‐SSIM‐Integration.