Abstract
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
Abstract (translated)
精确相机跟踪、高保真度的3D组织重建和实时在线可视化对于内窥镜等体内医学成像设备至关重要。然而，现有的SLAM（同时定位与映射）方法通常很难实现完整的手术野重建和高效的计算，限制了它们在内窥镜手术中的应用。在本文中，我们介绍了EndoGSLAM，一种用于内窥镜手术的高效SLAM方法，它将优化高斯表示和可导张量映射以实现在线相机跟踪和组织重建超过100帧/秒的渲染速度。大量的实验结果表明，EndoGSLAM比传统或神经SLAM方法在体内可用性和重建质量之间实现了更好的平衡，具有巨大的内窥镜手术潜力。项目页面位于https://www.endogslam.org/。
URL
https://arxiv.org/abs/2403.15124
