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
this https URL

EndoGSLAM 是一种用于内窥镜手术的高效 SLAM 方法，通过整合高速渲染和组织重建，实现在线相机跟踪和组织重建的 100 帧 / 秒渲染速度，具备优于传统或神经 SLAM 方法的手术过程中可用性和重建质量的平衡。