  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. We first focus on developing a fast NeRF-based surgical
scene 3D reconstruction approach that achieves state-of-the-art performance.
This method can significantly outperform traditional 3D reconstruction methods,
which have failed to capture large deformations and produce fine-grained shapes
and textures. We then propose an automated creation pipeline of interactive
surgical simulation environments through a closed mesh extraction algorithm.
Our experiments have validated the superior performance and efficiency of our
proposed approach in surgical scene 3D reconstruction. We further utilize our
reconstructed soft tissues to conduct FEM and MPM simulations, showcasing the
practical application of our method in data-driven surgical simulations.
