Zheng Yuchao, Zhang Jianing, Ning Guochen, Liao Hongen
Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11253691.
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/.
术中导航严重依赖精确的三维重建，以确保手术过程中的准确性和安全性。然而，内窥镜场景带来了独特的挑战，包括特征稀疏和光照不一致，这使得许多现有的基于运动结构（SfM）的方法不够充分，并且容易出现重建失败。为了减轻这些限制，我们提出了SurGSplat，这是一种通过整合几何约束来逐步细化三维高斯点云（3DGS）的新范式。通过能够详细重建血管结构和其他关键特征，SurGSplat为外科医生提供了更高的视觉清晰度，便于精确的术中决策。实验评估表明，SurGSplat在新视图合成（NVS）和姿态估计精度方面均取得了卓越的性能，使其成为手术场景重建的高保真且高效的解决方案。更多信息和结果可在页面https://surgsplat.github.io/上找到。
