发表机构
United Imaging Intelligence; Clemson University（联影智能; 克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出内窥镜4D几何基础模型SCOPE-4D，联合预测相机参数、密集几何与3D组织轨迹，利用大规模监督和运动约束提升几何估计与跟踪性能。
AI 中文摘要
几何理解支持内窥镜导航和机器人辅助，但学习可靠的内窥镜几何面临两个挑战：几何标注稀缺以及相机运动与组织变形之间的歧义。我们提出了SCOPE-4D，一个内窥镜4D几何基础模型，它在前向传播中从单目RGB视频联合预测相机参数、密集几何和3D组织轨迹。我们的策划和标注流程构建了SCOPE-5K，一个包含约5000个片段的集合，涵盖真实和合成胃肠道内窥镜及腹腔镜检查。该集合提供丰富的几何监督，并包括新收集的体模和真实结肠镜检查评估集。在SCOPE-5K上的几何监督微调学习内窥镜先验，改善了相机和深度估计。公共残差运动（CRM）进一步约束了相对于公共组织运动的局部变形。结合几何监督，CRM和轨迹监督进一步改善了相机和深度估计，优于仅几何微调，同时实现密集3D组织跟踪。对公共和新收集基准的评估展示了强大的域内和域外几何性能、优越的3D跟踪以及更稳定的长序列结肠重建。一项盲法用户研究进一步支持了在真实临床视频上感知的重建质量。这些结果共同证明了大规模内窥镜监督和运动约束对联合几何估计和组织跟踪的价值。
英文摘要
Geometric understanding supports endoscopic navigation and robotic assistance, but learning reliable endoscopic geometry faces two challenges: scarce geometric annotations and ambiguity between camera motion and tissue deformation. We present SCOPE-4D, an endoscopic 4D geometry foundation model that jointly predicts camera parameters, dense geometry, and 3D tissue trajectories from monocular RGB video in a single forward pass. Our curation and annotation pipeline constructs SCOPE-5K, a collection of approximately 5,000 clips spanning real and synthetic gastrointestinal endoscopy and laparoscopy. The collection provides rich geometric supervision and includes newly collected phantom and real-colonoscopy evaluation sets. Geometric supervised fine-tuning on SCOPE-5K learns endoscopic priors that improve camera and depth estimation. Common--Residual Motion (CRM) further constrains local deformation relative to common tissue movement. Together with geometric supervision, CRM and trajectory supervision further improve camera and depth estimation over geometric fine-tuning alone while enabling dense 3D tissue tracking. Evaluations on public and newly collected benchmarks demonstrate strong in-domain and out-of-domain geometry, superior 3D tracking, and more stable long-sequence colon reconstruction. A blinded user study further supports the perceived reconstruction quality on real clinical video. Together, these results demonstrate the value of large-scale endoscopic supervision and motion constraints for joint geometry estimation and tissue tracking.
CommentsProject page: https://chaoyizh.github.io/SCOPE-4D-page/
