SurGSplat：用于手术场景重建的渐进式几何约束高斯泼溅
SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction
AI总结：
针对内窥镜场景特征稀疏和光照不一致导致重建失败的问题，提出SurGSplat，通过整合几何约束渐进式细化3D高斯泼溅，实现血管等关键特征的高保真重建，显著提升新视角合成与位姿估计精度。
摘要：
术中导航严重依赖精确的3D重建，以确保手术过程中的准确性与安全性。然而，内窥镜场景带来了独特的挑战，包括稀疏特征和不一致的光照，这使得许多现有的基于运动恢复结构（SfM）的方法不足以胜任，且容易导致重建失败。为了缓解这些限制，我们提出了SurGSplat，这是一种新颖的范式，旨在通过整合几何约束来渐进式地细化3D高斯泼溅（3DGS）。通过实现对血管结构和其他关键特征的详细重建，SurGSplat为外科医生提供了增强的视觉清晰度，从而促进精确的术中决策。实验评估表明，SurGSplat在新视角合成（NVS）和位姿估计精度方面均取得了卓越的性能，确立了其作为手术场景重建的高保真和高效解决方案的地位。更多信息与结果可访问页面 https://surgsplat.github.io/。
英文摘要：
Intraoperative navigation relies heavily on precise 3D reconstruction to ensure accuracy and safety during surgical procedures. However, endoscopic scenarios present unique challenges, including sparse features and inconsistent lighting, which render many existing Structure-from-Motion (SfM)-based methods inadequate and prone to reconstruction failure. To mitigate these constraints, we propose SurGSplat, a novel paradigm designed to progressively refine 3D Gaussian Splatting (3DGS) through the integration of geometric constraints. By enabling the detailed reconstruction of vascular structures and other critical features, SurGSplat provides surgeons with enhanced visual clarity, facilitating precise intraoperative decision-making. Experimental evaluations demonstrate that SurGSplat achieves superior performance in both novel view synthesis (NVS) and pose estimation accuracy, establishing it as a high-fidelity and efficient solution for surgical scene reconstruction. More information and results can be found on the page https://surgsplat.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
