论文标题
c $^3 $融合：一致的对比结肠融合，朝着结肠镜检查深猛击
C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy
论文作者
论文摘要
来自光学结肠镜检查（OC）以检测未检查表面的3D结肠重建仍然是一个未解决的问题。挑战是由光学结肠镜数据的性质引起的，其特征是高度反射性低文字表面，剧烈的照明变化和频繁的跟踪损失。最近的方法表明了令人信服的结果，但遭受了：（1）易碎的框架到框架（或框架对模型）姿势估计，导致许多跟踪故障；或（2）以扫描质量为代价依靠基于点的表示。在本文中，我们提出了一个新颖的重建框架，该框架端到头地解决了这些问题，从而导致定量和定性上的3D结肠重建。我们的SLAM方法采用基于对比的深度特征以及深度一致的深度图，估计全球优化的姿势，能够从频繁跟踪失败中恢复并估算全局一致的3D模型，该方法采用对应关系；所有这些都在一个框架内。我们对多个合成和真实的结肠镜检查视频进行了广泛的实验评估，显示了与相关基线的高质量结果和比较。
3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. The challenges arise from the nature of optical colonoscopy data, characterized by highly reflective low-texture surfaces, drastic illumination changes and frequent tracking loss. Recent methods demonstrate compelling results, but suffer from: (1) frangible frame-to-frame (or frame-to-model) pose estimation resulting in many tracking failures; or (2) rely on point-based representations at the cost of scan quality. In this paper, we propose a novel reconstruction framework that addresses these issues end to end, which result in both quantitatively and qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach, which employs correspondences based on contrastive deep features, and deep consistent depth maps, estimates globally optimized poses, is able to recover from frequent tracking failures, and estimates a global consistent 3D model; all within a single framework. We perform an extensive experimental evaluation on multiple synthetic and real colonoscopy videos, showing high-quality results and comparisons against relevant baselines.
