  Surgical 3D reconstruction is a critical area of research in robotic surgery,
with recent works adopting variants of dynamic radiance fields to achieve
success in 3D reconstruction of deformable tissues from single-viewpoint
videos. However, these methods often suffer from time-consuming optimization or
inferior quality, limiting their adoption in downstream tasks. Inspired by 3D
Gaussian Splatting, a recent trending 3D representation, we present EndoGS,
applying Gaussian Splatting for deformable endoscopic tissue reconstruction.
Specifically, our approach incorporates deformation fields to handle dynamic
scenes, depth-guided supervision with spatial-temporal weight masks to optimize
3D targets with tool occlusion from a single viewpoint, and surface-aligned
regularization terms to capture the much better geometry. As a result, EndoGS
reconstructs and renders high-quality deformable endoscopic tissues from a
single-viewpoint video, estimated depth maps, and labeled tool masks.
Experiments on DaVinci robotic surgery videos demonstrate that EndoGS achieves
superior rendering quality. Code is available at
https://github.com/HKU-MedAI/EndoGS.
