ロボット手術における変形組織の3次元再構成技術のレビュー
A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery
ロボット手術の内視鏡映像から変形する組織を3次元再構成するNeRFや3Dガウシアンスプラッティングなどの最新手法をレビューし、2つのデータセットで評価した。
著者: Mengya Xu, Ziqi Guo, An Wang, Long Bai, Hongliang Ren
分類: cs.CV, cs.RO
原文アブストラクト
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.
