  Dense depth estimation from a single image is a key problem in computer
vision, with exciting applications in a multitude of robotic tasks. Initially
viewed as a direct regression problem, requiring annotated labels as
supervision at training time, in the past few years a substantial amount of
work has been done in self-supervised depth training based on strong geometric
cues, both from stereo cameras and more recently from monocular video
sequences. In this paper we investigate how these two approaches (supervised &
self-supervised) can be effectively combined, so that a depth model can learn
to encode true scale from sparse supervision while achieving high fidelity
local accuracy by leveraging geometric cues. To this end, we propose a novel
supervised loss term that complements the widely used photometric loss, and
show how it can be used to train robust semi-supervised monocular depth
estimation models. Furthermore, we evaluate how much supervision is actually
necessary to train accurate scale-aware monocular depth models, showing that
with our proposed framework, very sparse LiDAR information, with as few as 4
beams (less than 100 valid depth values per image), is enough to achieve
results competitive with the current state-of-the-art.
