  We present a robust and accurate depth refinement system, named GeoRefine,
for geometrically-consistent dense mapping from monocular sequences. GeoRefine
consists of three modules: a hybrid SLAM module using learning-based priors, an
online depth refinement module leveraging self-supervision, and a global
mapping module via TSDF fusion. The proposed system is online by design and
achieves great robustness and accuracy via: (i) a robustified hybrid SLAM that
incorporates learning-based optical flow and/or depth; (ii) self-supervised
losses that leverage SLAM outputs and enforce long-term geometric consistency;
(iii) careful system design that avoids degenerate cases in online depth
refinement. We extensively evaluate GeoRefine on multiple public datasets and
reach as low as $5\%$ absolute relative depth errors.
