SCOPE-4D: Endoscopic 4D Geometry Foundation Models
SCOPE-4D: Endoscopic 4D Geometry Foundation Models
Abstract: 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.
摘要： 几何理解是内窥镜导航和机器人辅助手术的基础，但学习可靠的内窥镜几何结构面临两大挑战：几何标注稀缺，以及相机运动与组织形变之间的模糊性。
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.
我们提出了 SCOPE-4D，这是一种内窥镜 4D 几何基础模型，它能够在单次前向传播中，从单目 RGB 视频中联合预测相机参数、稠密几何结构以及 3D 组织轨迹。
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.
我们的整理与标注流程构建了 SCOPE-5K 数据集，该数据集包含约 5,000 个片段，涵盖了真实及合成的胃肠道内窥镜和腹腔镜影像。该集合提供了丰富的几何监督信息，并包含了新采集的模体（phantom）和真实结肠镜检查评估集。
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.
通过在 SCOPE-5K 上进行几何监督微调，模型学习到了能够改善相机位姿和深度估计的内窥镜先验知识。通用-残差运动（Common-Residual Motion, CRM）机制进一步约束了相对于通用组织运动的局部形变。
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.
结合几何监督，CRM 和轨迹监督在单纯几何微调的基础上进一步提升了相机和深度估计的精度，同时实现了稠密的 3D 组织追踪。
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.
在公开和新采集基准测试上的评估表明，该模型在域内和域外均表现出强大的几何理解能力，具备卓越的 3D 追踪性能，并能实现更稳定的长序列结肠重建。一项盲测用户研究进一步证实了其在真实临床视频中感知的重建质量。
Together, these results demonstrate the value of large-scale endoscopic supervision and motion constraints for joint geometry estimation and tissue tracking.
总之，这些结果证明了大规模内窥镜监督和运动约束对于联合几何估计与组织追踪的价值。