  3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined
surfaces remains an unsolved problem. The challenges arise from the nature of
optical colonoscopy data, characterized by highly reflective low-texture
surfaces, drastic illumination changes and frequent tracking loss. Recent
methods demonstrate compelling results, but suffer from: (1) frangible
frame-to-frame (or frame-to-model) pose estimation resulting in many tracking
failures; or (2) rely on point-based representations at the cost of scan
quality. In this paper, we propose a novel reconstruction framework that
addresses these issues end to end, which result in both quantitatively and
qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach,
which employs correspondences based on contrastive deep features, and deep
consistent depth maps, estimates globally optimized poses, is able to recover
from frequent tracking failures, and estimates a global consistent 3D model;
all within a single framework. We perform an extensive experimental evaluation
on multiple synthetic and real colonoscopy videos, showing high-quality results
and comparisons against relevant baselines.
