  Today's state-of-the-art methods for 3D object detection are based on lidar,
stereo, or monocular cameras. Lidar-based methods achieve the best accuracy,
but have a large footprint, high cost, and mechanically-limited angular
sampling rates, resulting in low spatial resolution at long ranges. Recent
approaches based on low-cost monocular or stereo cameras promise to overcome
these limitations but struggle in low-light or low-contrast regions as they
rely on passive CMOS sensors. In this work, we propose a novel 3D object
detection modality that exploits temporal illumination cues from a low-cost
monocular gated imager. We propose a novel deep detector architecture, Gated3D,
that is tailored to temporal illumination cues from three gated images. Gated
images allow us to exploit mature 2D object feature extractors that guide the
3D predictions through a frustum segment estimation. We assess the proposed
method on a novel 3D detection dataset that includes gated imagery captured in
over 10,000 km of driving data. We validate that our method outperforms
state-of-the-art monocular and stereo approaches at long distances. We will
release our code and dataset, opening up a new sensor modality as an avenue to
replace lidar in autonomous driving.
