  Dynamic reconstruction of deformable tissues in endoscopic video is a key
technology for robot-assisted surgery. Recent reconstruction methods based on
neural radiance fields (NeRFs) have achieved remarkable results in the
reconstruction of surgical scenes. However, based on implicit representation,
NeRFs struggle to capture the intricate details of objects in the scene and
cannot achieve real-time rendering. In addition, restricted single view
perception and occluded instruments also propose special challenges in surgical
scene reconstruction. To address these issues, we develop SurgicalGaussian, a
deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our
approach models the spatio-temporal features of soft tissues at each time stamp
via a forward-mapping deformation MLP and regularization to constrain local 3D
Gaussians to comply with consistent movement. With the depth initialization
strategy and tool mask-guided training, our method can remove surgical
instruments and reconstruct high-fidelity surgical scenes. Through experiments
on various surgical videos, our network outperforms existing method on many
aspects, including rendering quality, rendering speed and GPU usage. The
project page can be found at https://surgicalgaussian.github.io.
