  Visualizing colonoscopy is crucial for medical auxiliary diagnosis to prevent
undetected polyps in areas that are not fully observed. Traditional
feature-based and depth-based reconstruction approaches usually end up with
undesirable results due to incorrect point matching or imprecise depth
estimation in realistic colonoscopy videos. Modern deep-based methods often
require a sufficient number of ground truth samples, which are generally hard
to obtain in optical colonoscopy. To address this issue, self-supervised and
domain adaptation methods have been explored. However, these methods neglect
geometry constraints and exhibit lower accuracy in predicting detailed depth.
We thus propose a novel reconstruction pipeline with a bi-directional
adaptation architecture named ToDER to get precise depth estimations.
Furthermore, we carefully design a TNet module in our adaptation architecture
to yield geometry constraints and obtain better depth quality. Estimated depth
is finally utilized to reconstruct a reliable colon model for visualization.
Experimental results demonstrate that our approach can precisely predict depth
maps in both realistic and synthetic colonoscopy videos compared with other
self-supervised and domain adaptation methods. Our method on realistic
colonoscopy also shows the great potential for visualizing unobserved regions
and preventing misdiagnoses.
