A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery

TL;DR

This paper reviews recent 3D reconstruction techniques for deformable tissues in robotic surgery, highlighting their innovations, challenges, and potential for real-time clinical application.

Contribution

It provides a comprehensive review and empirical evaluation of NeRF-based and Gaussian splatting methods for surgical scene reconstruction.

Findings

NeRF-based techniques face challenges with slow inference and dynamic scenes.

Gaussian splatting offers explicit scene representation, improving reconstruction speed.

Advancements enable potential real-time, high-quality surgical scene reconstruction.

Abstract

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.