{"title":"基于结构相似度测度积分的立体内窥镜相机位姿优化估计","authors":"Ruoqi Lian, Wei Li, Junchen Hao, Yanfang Zhang, Fucang Jia","doi":"10.1002/rcs.70078","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The pose estimation accuracy has been improved by incorporating SSIM. The code is available at: https://github.com/lianrq/pose-estimation-by-SSIM-Integration.</p>\n </section>\n </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 3","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stereo Endoscopic Camera Pose Optimal Estimation by Structure Similarity Index Measure Integration\",\"authors\":\"Ruoqi Lian, Wei Li, Junchen Hao, Yanfang Zhang, Fucang Jia\",\"doi\":\"10.1002/rcs.70078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>The pose estimation accuracy has been improved by incorporating SSIM. The code is available at: https://github.com/lianrq/pose-estimation-by-SSIM-Integration.</p>\\n </section>\\n </div>\",\"PeriodicalId\":50311,\"journal\":{\"name\":\"International Journal of Medical Robotics and Computer Assisted Surgery\",\"volume\":\"21 3\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Robotics and Computer Assisted Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rcs.70078\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Robotics and Computer Assisted Surgery","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rcs.70078","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}
Stereo Endoscopic Camera Pose Optimal Estimation by Structure Similarity Index Measure Integration
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.
期刊介绍：
The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.
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