Abstract
Computer vision techniques have the potential to improve the diagnostic performance of colonoscopy, but the lack of 3D colonoscopy datasets for training and validation hinders their development. This paper introduces C3VDv2, the second version (v2) of the high-definition Colonoscopy 3D Video Dataset, featuring enhanced realism designed to facilitate the quantitative evaluation of 3D colon reconstruction algorithms. 192 video sequences were captured by imaging 60 unique, high-fidelity silicone colon phantom segments. Ground truth depth, surface normals, optical flow, occlusion, six-degree-of-freedom pose, coverage maps, and 3D models are provided for 169 colonoscopy videos. Eight simulated screening colonoscopy videos acquired by a gastroenterologist are provided with ground truth poses. The dataset includes 15 videos featuring colon deformations for qualitative assessment. C3VDv2 emulates diverse and challenging scenarios for 3D reconstruction algorithms, including fecal debris, mucous pools, blood, debris obscuring the colonoscope lens, en-face views, and fast camera motion. The enhanced realism of C3VDv2 will allow for more robust and representative development and evaluation of 3D reconstruction algorithms.
Abstract (translated)
计算机视觉技术有潜力提升结肠镜检查的诊断性能，但缺乏用于训练和验证的3D结肠镜数据集阻碍了其发展。本文介绍了C3VDv2，即高清晰度结肠镜3D视频数据集的第二版（v2），该版本增强了现实感设计，旨在促进三维结肠重建算法的定量评估。通过成像60个独特的、高度逼真的硅胶结肠模型片段，采集了192段视频序列。对于其中的169段结肠镜检查视频提供了地面真实深度信息、表面法线、光学流、遮挡情况、六自由度姿态、覆盖图和三维模型等数据。另外还提供了由胃肠病医生获取的八段模拟筛查结肠镜检查视频，这些视频附有真实的姿态数据。该数据集还包括了15段展示结肠变形的视频，用于定性评估。 C3VDv2模拟了多种复杂场景以挑战3D重建算法，包括粪便残渣、黏液池、血液、遮挡内窥镜镜头的碎片、正面视图以及快速相机移动。C3VDv2增强的真实感将有助于开发和评估更加稳健且具有代表性的三维重建算法。
URL
https://arxiv.org/abs/2506.24074
