A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery
It addresses the problem of slow and occluded reconstructions for surgeons in robotic minimally invasive surgery, but is incremental as a review and replication study.
This paper reviews state-of-the-art 3D reconstruction techniques for deformable tissues in robotic surgery, focusing on methods like NeRF and 3D-GS, and finds that advancements enable feasible real-time, high-quality reconstructions.
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.