Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots. However, existing SLAM (Simultaneous Localization and Mapping) methods often struggle to achieve both complete high-quality surgical field reconstruction and efficient computation, restricting their intraoperative applications among endoscopic surgeries. In this paper, we introduce EndoGSLAM, an efficient SLAM approach for endoscopic surgeries, which integrates streamlined Gaussian representation and differentiable rasterization to facilitate over 100 fps rendering speed during online camera tracking and tissue reconstructing. Extensive experiments show that EndoGSLAM achieves a better trade-off between intraoperative availability and reconstruction quality than traditional or neural SLAM approaches, showing tremendous potential for endoscopic surgeries. The project page is at https://EndoGSLAM.loping151.com

翻译：精确的相机追踪、高保真三维组织重建和实时在线可视化对于内窥镜及胶囊机器人等体内医疗成像设备至关重要。然而，现有的同时定位与地图构建（SLAM）方法常难以兼顾高质量手术视野重建与高效计算，限制了其在内窥镜手术中的术中应用。本文提出EndoGSLAM——一种面向内窥镜手术的高效SLAM方法，该方法融合了精简的高斯表示与可微分光栅化技术，在在线相机追踪和组织重建过程中可实现超过100帧/秒的渲染速度。大量实验表明，与传统或神经SLAM方法相比，EndoGSLAM在术中可用性与重建质量之间取得了更优的平衡，展现出内窥镜手术领域的巨大潜力。项目页面为https://EndoGSLAM.loping151.com。