发表机构
Texas A&M University; University of North Carolina at Chapel Hill; University of Minnesota; University of Texas at Austin（德克萨斯农工大学; 北卡罗来纳大学教堂山分校; 明尼苏达大学; 德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EndoMD-SLAM是针对内窥镜光学退化问题的SLAM框架，通过记忆驱动门控、静态-瞬态分解等机制，在结肠镜检查基准上实现了轨迹误差降低91%、PSNR提升9.9 dB的效果。
AI 中文摘要
密集三维重建对于临床内窥镜导航和文档记录至关重要。虽然高斯溅射SLAM系统在该领域展现出应用前景，但它们从根本上依赖严格的多视图光度一致性假设。在常规操作中，这一假设会因移动碎屑、冲水等间歇性光学退化而被严重违背，标准系统会错误地将这些附着于相机的伪影融合到持久三维几何中，导致严重的跟踪漂移和不可逆的地图损坏。为解决这一局限，我们提出EndoMD-SLAM，这是一个通过专用跟踪与建图机制在光学退化下维持稳定性的框架。在跟踪端，记忆驱动的门控机制检测不可靠观测以暂停地图更新，并利用历史关键帧进行感知漂移的重定位；在建图端，自监督静态-瞬态分解将视觉伪影隔离到专用瞬态场中，这种显式分离可防止伪影与持久解剖地图在结构上纠缠。我们从结肠镜检查视频中整理出聚焦退化的基准，以系统评估这些失效模式。大量实验表明，标准基线在严重光学退化下失效，而EndoMD-SLAM可保持几何完整性，将绝对轨迹误差降低91%，并将渲染保真度提升9.9 dB的PSNR。
英文摘要
Dense 3D reconstruction is critical for clinical endoscopic navigation and documentation. While Gaussian Splatting SLAM systems show promise in this domain, they fundamentally rely on strict multi-view photometric consistency. In routine procedures, this assumption is severely violated by intermittent optical degradations like moving debris and water flushing. Standard systems erroneously fuse these cameraattached artifacts into the persistent 3D geometry, causing severe tracking drift and irreversible map corruption. To address this limitation, we propose EndoMD-SLAM, a framework designed to maintain stability under optical degradation through specialized tracking and mapping mechanisms. On the tracking side, a memory-driven gating mechanism detects unreliable observations to suspend map updates and utilizes historical keyframes for drift-aware relocalization. On the mapping side, a self-supervised static-transient decomposition isolates visual contaminants into a dedicated transient field. This explicit separation prevents artifacts from structurally entangling with the persistent anatomical map. We curate a degradationfocused benchmark from colonoscopy videos to systematically evaluate these failure modes. Extensive experiments show that while standard baselines fail under severe optical degradation, EndoMD-SLAM preserves geometric integrity, reducing absolute trajectory error by 91% and improving rendering fidelity by 9.9 dB PSNR.
CommentsProject page: https://endomd-slam.github.io/
