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Reconstruction of deformable scenes from endoscopic videos is important formany applications such as intraoperative navigation, surgical visualperception, and robotic surgery. It is a foundational requirement for realizingautonomous robotic interventions for minimally invasive surgery. However,previous approaches in this domain have been limited by their modular natureand are confined to specific camera and scene settings. Our work adopts theNeural Radiance Fields (NeRF) approach to learning 3D implicit representationsof scenes that are both dynamic and deformable over time, and furthermore withunknown camera poses. We demonstrate this approach on endoscopic surgicalscenes from robotic surgery. This work removes the constraints of known cameraposes and overcomes the drawbacks of the state-of-the-art unstructured dynamicscene reconstruction technique, which relies on the static part of the scenefor accurate reconstruction. Through several experimental datasets, wedemonstrate the versatility of our proposed model to adapt to diverse cameraand scene settings, and show its promise for both current and future roboticsurgical systems.

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