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。

Deform3DGS: 高斯点云绘制技术的灵活变形用于快速手术场景重建

Deform3DGS: Flexible Deformation for Fast Surgical Scene Reconstruction  with Gaussian Splatting

Reconstructing deformable tissues from endoscopic stereo videos is essential
in many downstream surgical applications. However, existing methods suffer from
slow inference speed, which greatly limits their practical use. In this paper,
we introduce EndoGaussian, a real-time surgical scene reconstruction framework
that builds on 3D Gaussian Splatting. Our framework represents dynamic surgical
scenes as canonical Gaussians and a time-dependent deformation field, which
predicts Gaussian deformations at novel timestamps. Due to the efficient
Gaussian representation and parallel rendering pipeline, our framework
significantly accelerates the rendering speed compared to previous methods. In
addition, we design the deformation field as the combination of a lightweight
encoding voxel and an extremely tiny MLP, allowing for efficient Gaussian
tracking with a minor rendering burden. Furthermore, we design a holistic
Gaussian initialization method to fully leverage the surface distribution
prior, achieved by searching informative points from across the input image
sequence. Experiments on public endoscope datasets demonstrate that our method
can achieve real-time rendering speed (195 FPS real-time, 100$\times$ gain)
while maintaining the state-of-the-art reconstruction quality (35.925 PSNR) and
the fastest training speed (within 2 min/scene), showing significant promise
for intraoperative surgery applications. Code is available at:
https://yifliu3.github.io/EndoGaussian/.

通过使用 3D 高斯泼洒方法，我们引入了实时外科手术场景重建框架 EndoGaussian，能够在现实时间内实现手术场景的重建，实现了很大的渲染加速，同时在保持最新的重建质量和最快的训练速度方面表现出显著的优势。