  Vision-based depth estimation is a key feature in autonomous systems, which
often relies on a single camera or several independent ones. In such a
monocular setup, dense depth is obtained with either additional input from one
or several expensive LiDARs, e.g., with 64 beams, or camera-only methods, which
suffer from scale-ambiguity and infinite-depth problems. In this paper, we
propose a new alternative of densely estimating metric depth by combining a
monocular camera with a light-weight LiDAR, e.g., with 4 beams, typical of
today's automotive-grade mass-produced laser scanners. Inspired by recent
self-supervised methods, we introduce a novel framework, called LiDARTouch, to
estimate dense depth maps from monocular images with the help of ``touches'' of
LiDAR, i.e., without the need for dense ground-truth depth. In our setup, the
minimal LiDAR input contributes on three different levels: as an additional
model's input, in a self-supervised LiDAR reconstruction objective function,
and to estimate changes of pose (a key component of self-supervised depth
estimation architectures). Our LiDARTouch framework achieves new state of the
art in self-supervised depth estimation on the KITTI dataset, thus supporting
our choices of integrating the very sparse LiDAR signal with other visual
features. Moreover, we show that the use of a few-beam LiDAR alleviates scale
ambiguity and infinite-depth issues that camera-only methods suffer from. We
also demonstrate that methods from the fully-supervised depth-completion
literature can be adapted to a self-supervised regime with a minimal LiDAR
signal.
