论文标题
鼠尾草：内窥镜检查之前的外观和几何形状
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
论文作者
论文摘要
在内窥镜检查中，许多应用（例如，手术导航）将受益于一种实时方法，该方法可以同时跟踪内窥镜并从单眼内窥镜视频中重建观察到的解剖结构的密集3D几何形状。为此，我们通过组合基于学习的外观和优化的几何学先验和因子图优化来开发同时的本地化和映射系统。在端到端的可区分训练管道中明确学习了外观和几何学先验，以掌握配对图像对齐的任务，这是SLAM系统的核心组件之一。在我们的实验中，提出的大满贯系统被证明可以牢固地应对内窥镜检查中通常看到的质地稀缺和照明变化的挑战。该系统可以很好地概括为看不见的内窥镜和受试者，并且与基于最新功能的大满贯系统相比表现出色。代码存储库可在https://github.com/lppllppl920/sage-slam.git上找到。
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry of the observed anatomy from a monocular endoscopic video. To this end, we develop a Simultaneous Localization and Mapping system by combining the learning-based appearance and optimizable geometry priors and factor graph optimization. The appearance and geometry priors are explicitly learned in an end-to-end differentiable training pipeline to master the task of pair-wise image alignment, one of the core components of the SLAM system. In our experiments, the proposed SLAM system is shown to robustly handle the challenges of texture scarceness and illumination variation that are commonly seen in endoscopy. The system generalizes well to unseen endoscopes and subjects and performs favorably compared with a state-of-the-art feature-based SLAM system. The code repository is available at https://github.com/lppllppl920/SAGE-SLAM.git.
