SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction

TL;DR

SurGSplat introduces a progressive, geometry-constrained Gaussian Splatting method that significantly improves 3D reconstruction accuracy and visual clarity in challenging intraoperative endoscopic scenarios, aiding surgical navigation.

Contribution

It presents a novel framework that enhances 3D Gaussian Splatting with geometric constraints for better surgical scene reconstruction.

Findings

Outperforms existing methods in novel view synthesis

Achieves higher pose estimation accuracy

Provides detailed reconstruction of vascular structures

Abstract

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/.