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.

通过将 3D GS 集成到手术场景中并引入新的灵活变形建模方案（FDM），本文提出了一种名为 Deform3DGS 的快速重建框架，该框架能够在内窥镜手术中对可变形组织进行实时重建，加速手术现场重建并在临床中具有重要的临床应用价值。