BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields

Shreya Saha, Zekai Liang, Shan Lin, Jingpei Lu, Michael Yip, Sainan Liu; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3003-3012

Reconstruction of deformable scenes from endoscopic videos is important for many applications such as intraoperative navigation surgical visual perception and robotic surgery. It is a foundational requirement for realizing autonomous robotic interventions for minimally invasive surgery. However previous approaches in this domain have been limited by their modular nature and are confined to specific camera and scene settings. Our work adopts the Neural Radiance Fields (NeRF) approach to learning 3D implicit representations of scenes that are both dynamic and deformable over time and furthermore with unknown camera poses. This work removes the constraints of known camera poses and overcomes the drawbacks of the state-of-the-art unstructured dynamic scene reconstruction technique which relies on the static part of the scene for accurate reconstruction. Through several experimental datasets we demonstrate the versatility of our proposed model to adapt to diverse camera and scene settings and show its promise for both current and future robotic surgical systems.