实时重建内窥镜视频3D场景并跟踪组织变形，助力手术辅助。
Online 3D reconstruction and dense tracking in endoscopic videos
- 用高斯点云动态扩展场景表示，结合控制点建模组织形变。
- 在StereoMIS数据集上跟踪精度优于现有方法，接近离线重建水平。
- 适合需要实时3D视觉反馈的微创手术系统研发者。
从双目内窥镜视频数据中进行3D场景重建对推进手术干预至关重要。本文提出一种在线框架，实现密集3D场景重建与稳定跟踪，旨在提升手术场景理解并辅助干预操作。方法通过高斯点云动态扩展标准场景表示，并利用稀疏控制点建模组织形变。设计了一种高效的在线拟合算法，优化场景参数，实现一致跟踪与精确重建。在StereoMIS数据集上的实验表明，该方法在跟踪性能上超越现有最先进方法，重建效果可媲美离线重建技术。本工作为多种下游应用提供支持，推动手术辅助系统能力提升。
原文摘要 · Abstract (English)
3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene reconstruction and tracking, aimed at enhancing surgical scene understanding and assisting interventions. Our method dynamically extends a canonical scene representation using Gaussian splatting, while modeling tissue deformations through a sparse set of control points. We introduce an efficient online fitting algorithm that optimizes the scene parameters, enabling consistent tracking and accurate reconstruction. Through experiments on the StereoMIS dataset, we demonstrate the effectiveness of our approach, outperforming state-of-the-art tracking methods and achieving comparable performance to offline reconstruction techniques. Our work enables various downstream applications thus contributing to advancing the capabilities of surgical assistance systems.
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