EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting
This addresses the need for precise intraoperative visualization in endoscopic surgeries, offering a more efficient solution than traditional or neural SLAM methods, though it appears incremental as it builds on existing SLAM and Gaussian splatting techniques.
The paper tackles the problem of achieving real-time dense reconstruction and tracking in endoscopic surgeries, which existing SLAM methods struggle with due to trade-offs between quality and efficiency. It introduces EndoGSLAM, which integrates Gaussian representation and differentiable rasterization to achieve over 100 fps rendering speed while maintaining high reconstruction quality.
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