C3VDv2 -- Colonoscopy 3D video dataset with enhanced realism
Mayank V. Golhar, Lucas Sebastian Galeano Fretes, Loren Ayers, Venkata S. Akshintala, Taylor L. Bobrow, Nicholas J. Durr

TL;DR
C3VDv2 is a comprehensive high-definition 3D colonoscopy video dataset with enhanced realism, designed to advance the development and evaluation of 3D reconstruction algorithms in realistic clinical scenarios.
Contribution
The paper introduces C3VDv2, a new dataset with diverse, high-fidelity colonoscopy videos and ground truth data, addressing the lack of realistic 3D colonoscopy datasets.
Findings
Provides 192 high-quality videos with ground truth annotations.
Includes challenging scenarios like debris and mucous pools.
Enables robust evaluation of 3D reconstruction algorithms.
Abstract
Spatial computer vision techniques have the potential to improve the diagnostic performance of colonoscopy. However, 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 totaling 169,371 frames were captured by imaging 60 unique, high-fidelity silicone colon phantom segments. Ground truth depth, surface normals, optical flow, occlusion, diffuse maps, six-degree-of-freedom pose, coverage map, and 3D models are provided for 169 colonoscopy videos. Eight simulated screening colonoscopy videos acquired by a gastroenterologist are provided with ground truth poses. Lastly, the dataset includes 15 videos with colon…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsColorectal Cancer Screening and Detection · AI in cancer detection · Advanced Image and Video Retrieval Techniques
