DyEndoVO: scene dynamics-aware pose estimation of endoscope in minimally invasive surgery
In this work, we aim to improve the accuracy and robustness of pose estimation in challenging dynamic surgical scenes, by effectively handling scene dynamics. Our method, combined with the proposed synthetic dataset, demonstrates improved performance in pose estimation and generalizes well to real-world data, showing its potential in advancing related works such as SLAM and 3D reconstruction in complex surgical environments.
Authors
- Marius Distler (ORCID: https://orcid.org/0000-0002-3164-5755)
- Martin Wagner (ORCID: https://orcid.org/0000-0002-9831-9110)
- Reuben Docea
- Micha Pfeiffer (ORCID: https://orcid.org/0000-0001-9822-8641)
- Stefanie Speidel
- Jinjing Xu
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- National Center for Tumor Diseases (DE)
- University Hospital Carl Gustav Carus (DE)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1007/s11548-025-03549-0
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Deutsche Forschungsgemeinschaft
- HORIZON EUROPE Framework Programme