Efficient and high-fidelity reconstruction of deformable surgical scenes is a critical yet challenging task. Building on recent advancements in 3D Gaussian splatting, current methods have seen significant improvements in both reconstruction quality and rendering speed. However, two major limitations remain: (1) difficulty in handling irreversible dynamic changes, such as tissue shearing, which are common in surgical scenes; and (2) the lack of hierarchical modeling for surgical scene deformation, which reduces rendering speed. To address these challenges, we introduce EH-SurGS, an efficient and high-fidelity reconstruction algorithm for deformable surgical scenes. We propose a deformation modeling approach that incorporates the life cycle of 3D Gaussians, effectively capturing both regular and irreversible deformations, thus enhancing reconstruction quality. Additionally, we present an adaptive motion hierarchy strategy that distinguishes between static and deformable regions within the surgical scene. This strategy reduces the number of 3D Gaussians passing through the deformation field, thereby improving rendering speed. Extensive experiments demonstrate that our method surpasses existing state-of-the-art approaches in both reconstruction quality and rendering speed. Ablation studies further validate the effectiveness and necessity of our proposed components.
对可变形手术场景的高效且高保真重建是一个关键而具有挑战性的任务。基于最近 3D 高斯喷射技术的进展，目前的方法在重建质量和渲染速度上取得了显著提升。然而，仍存在两大主要限制：(1) 难以处理不可逆的动态变化，例如手术场景中常见的组织剪切；(2) 缺乏对手术场景变形的分层建模，导致渲染速度降低。 为了解决这些问题，我们提出了 EH-SurGS，一种针对可变形手术场景的高效高保真重建算法。我们设计了一种变形建模方法，融入了 3D 高斯的生命周期管理，有效捕捉规则和不可逆变形，从而提升重建质量。此外，我们提出了一种自适应运动层次策略，能够区分手术场景中的静态区域和可变形区域。该策略减少了穿过变形场的 3D 高斯数量，从而提高渲染速度。 大量实验表明，我们的方法在重建质量和渲染速度上均优于现有最先进方法（SOTA）。消融实验进一步验证了所提出组件的有效性和必要性。