In the realm of robot-assisted minimally invasive surgery, dynamic scene
reconstruction can significantly enhance downstream tasks and improve surgical
outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to
prominence for their exceptional ability to reconstruct scenes. Nonetheless,
these methods are hampered by slow inference, prolonged training, and
substantial computational demands. Additionally, some rely on stereo depth
estimation, which is often infeasible due to the high costs and logistical
challenges associated with stereo cameras. Moreover, the monocular
reconstruction quality for deformable scenes is currently inadequate. To
overcome these obstacles, we present Endo-4DGS, an innovative, real-time
endoscopic dynamic reconstruction approach that utilizes 4D Gaussian Splatting
(GS) and requires no ground truth depth data. This method extends 3D GS by
incorporating a temporal component and leverages a lightweight MLP to capture
temporal Gaussian deformations. This effectively facilitates the reconstruction
of dynamic surgical scenes with variable conditions. We also integrate
Depth-Anything to generate pseudo-depth maps from monocular views, enhancing
the depth-guided reconstruction process. Our approach has been validated on two
surgical datasets, where it has proven to render in real-time, compute
efficiently, and reconstruct with remarkable accuracy. These results underline
the vast potential of Endo-4DGS to improve surgical assistance.

在机器人辅助微创手术领域，动态场景重建可以显著提高下游任务和改善手术结果。我们提出了一种创新的实时内窥镜动态重建方法 Endo-4DGS，使用了 4D 高斯飞溅和无需地面真实深度数据。该方法通过引入时间组件，借助轻量级 MLP 捕捉时间高斯变形，有效实现了具有可变条件的动态手术场景的重建。我们还整合了 Depth-Anything，从单目视图中生成伪深度图，增强了深度引导重建过程。我们的方法在两个手术数据集上通过验证，证明了实时渲染、高效计算和重建准确性，彰显了 Endo-4DGS 改善手术辅助的巨大潜力。