面向手术场景重建的渐进几何约束高斯溅射 (SurGSplat)
摘要
内手术导航高度依赖精确的 3D 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 presents 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/.
引用
@article{arxiv.2506.05935,
title = {SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction},
author = {Yuchao Zheng and Jianing Zhang and Guochen Ning and Hongen Liao},
journal= {arXiv preprint arXiv:2506.05935},
year = {2025}
}