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Screening colonoscopy is an important clinical application for several 3Dcomputer vision techniques, including depth estimation, surface reconstruction,and missing region detection. However, the development, evaluation, andcomparison of these techniques in real colonoscopy videos remain largelyqualitative due to the difficulty of acquiring ground truth data. In this work,we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a highdefinition clinical colonoscope and high-fidelity colon models for benchmarkingcomputer vision methods in colonoscopy. We introduce a novel multimodal 2D-3Dregistration technique to register optical video sequences with ground truthrendered views of a known 3D model. The different modalities are registered bytransforming optical images to depth maps with a Generative Adversarial Networkand aligning edge features with an evolutionary optimizer. This registrationmethod achieves an average translation error of 0.321 millimeters and anaverage rotation error of 0.159 degrees in simulation experiments whereerror-free ground truth is available. The method also leverages videoinformation, improving registration accuracy by 55.6% for translation and 60.4%for rotation compared to single frame registration. 22 short video sequenceswere registered to generate 10,015 total frames with paired ground truth depth,surface normals, optical flow, occlusion, six degree-of-freedom pose, coveragemaps, and 3D models. The dataset also includes screening videos acquired by agastroenterologist with paired ground truth pose and 3D surface models. Thedataset and registration source code are available at durr.jhu.edu/C3VD.

Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration