Efficient EndoNeRF reconstruction and its application for data-driven surgical simulation

Yuehao Wang, Bingchen Gong, Yonghao Long, Siu Hin Fan, Qi Dou

Abstract: The healthcare industry has a growing need for realistic modeling and efficient simulation of surgical scenes. With effective models of deformable surgical scenes, clinicians are able to conduct surgical planning and surgery training on scenarios close to real-world cases. However, a significant challenge in achieving such a goal is the scarcity of high-quality soft tissue models with accurate shapes and textures. To address this gap, we present a data-driven framework that leverages emerging neural radiance field technology to enable high-quality surgical reconstruction and explore its application for surgical simulations.