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 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 4.000 km driving in northern Europe.

@inproceedings{Gruber:2019:GRD,
   author = "Tobias Gruber and Frank Julca-Aguilar and Mario Bijelic and Werner
      Ritter and Klaus Dietmayer and Felix Heide",
   title = "{Gated2Depth}: Real-time Dense Lidar from Gated Images",
   booktitle = "International Conference on Computer Vision (ICCV) oral presentation",
   year = "2019",
   month = oct
}