  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 but are hampered
by slow inference speed, prolonged training, and inconsistent depth estimation.
Some previous work utilizes ground truth depth for optimization but is hard to
acquire in the surgical domain. To overcome these obstacles, we present
Endo-4DGS, a real-time endoscopic dynamic reconstruction approach that utilizes
3D Gaussian Splatting (GS) for 3D representation. Specifically, we propose
lightweight MLPs to capture temporal dynamics with Gaussian deformation fields.
To obtain a satisfactory Gaussian Initialization, we exploit a powerful depth
estimation foundation model, Depth-Anything, to generate pseudo-depth maps as a
geometry prior. We additionally propose confidence-guided learning to tackle
the ill-pose problems in monocular depth estimation and enhance the
depth-guided reconstruction with surface normal constraints and depth
regularization. Our approach has been validated on two surgical datasets, where
it can effectively render in real-time, compute efficiently, and reconstruct
with remarkable accuracy.
