  As a crucial and intricate task in robotic minimally invasive surgery,
reconstructing surgical scenes using stereo or monocular endoscopic video holds
immense potential for clinical applications. NeRF-based techniques have
recently garnered attention for the ability to reconstruct scenes implicitly.
On the other hand, Gaussian splatting-based 3D-GS represents scenes explicitly
using 3D Gaussians and projects them onto a 2D plane as a replacement for the
complex volume rendering in NeRF. However, these methods face challenges
regarding surgical scene reconstruction, such as slow inference, dynamic
scenes, and surgical tool occlusion. This work explores and reviews
state-of-the-art (SOTA) approaches, discussing their innovations and
implementation principles. Furthermore, we replicate the models and conduct
testing and evaluation on two datasets. The test results demonstrate that with
advancements in these techniques, achieving real-time, high-quality
reconstructions becomes feasible.
