  Colorectal cancer is one of the most common cancers in the world. While
colonoscopy is an effective screening technique, navigating an endoscope
through the colon to detect polyps is challenging. A 3D map of the observed
surfaces could enhance the identification of unscreened colon tissue and serve
as a training platform. However, reconstructing the colon from video footage
remains difficult. Learning-based approaches hold promise as robust
alternatives, but necessitate extensive datasets. Establishing a benchmark
dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate
data-driven depth and pose prediction during colonoscopy. The challenge was
hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and
representatives from academia and industry participated in the three
sub-challenges: synthetic depth prediction, synthetic pose prediction, and real
pose prediction. This paper describes the challenge, the submitted methods, and
their results. We show that depth prediction from synthetic colonoscopy images
is robustly solvable, while pose estimation remains an open research question.
