  We present an imaging framework which converts three images from a gated
camera into high-resolution depth maps with depth accuracy comparable to pulsed
lidar measurements. Existing scanning lidar systems achieve low spatial
resolution at large ranges due to mechanically-limited angular sampling rates,
restricting scene understanding tasks to close-range clusters with dense
sampling. Moreover, today's pulsed lidar scanners suffer from high cost, power
consumption, large form-factors, and they fail in the presence of strong
backscatter. We depart from point scanning and demonstrate that it is possible
to turn a low-cost CMOS gated imager into a dense depth camera with at least
80m range - by learning depth from three gated images. The proposed
architecture exploits semantic context across gated slices, and is trained on a
synthetic discriminator loss without the need of dense depth labels. The
proposed replacement for scanning lidar systems is real-time, handles
back-scatter and provides dense depth at long ranges. We validate our approach
in simulation and on real-world data acquired over 4,000km driving in northern
Europe. Data and code are available at https://github.com/gruberto/Gated2Depth.
