  Tissue deformation poses a key challenge for accurate surgical scene
reconstruction. Despite yielding high reconstruction quality, existing methods
suffer from slow rendering speeds and long training times, limiting their
intraoperative applicability. Motivated by recent progress in 3D Gaussian
Splatting, an emerging technology in real-time 3D rendering, this work presents
a novel fast reconstruction framework, termed Deform3DGS, for deformable
tissues during endoscopic surgery. Specifically, we introduce 3D GS into
surgical scenes by integrating a point cloud initialization to improve
reconstruction. Furthermore, we propose a novel flexible deformation modeling
scheme (FDM) to learn tissue deformation dynamics at the level of individual
Gaussians. Our FDM can model the surface deformation with efficient
representations, allowing for real-time rendering performance. More
importantly, FDM significantly accelerates surgical scene reconstruction,
demonstrating considerable clinical values, particularly in intraoperative
settings where time efficiency is crucial. Experiments on DaVinci robotic
surgery videos indicate the efficacy of our approach, showcasing superior
reconstruction fidelity PSNR: (37.90) and rendering speed (338.8 FPS) while
substantially reducing training time to only 1 minute/scene. Our code is
available at https://github.com/jinlab-imvr/Deform3DGS.
