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 using 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. We propose a novel 3D object detection modality that exploits temporal illumination cues from a low-cost monocular gated imager. We introduce a novel deep detection architecture, Gated3D, that is tailored to temporal illumination cues in gated images. This modality allows us to exploit mature 2D object feature extractors that guide the 3D predictions through a frustum segment estimation. We assess the proposed method experimentally on a 3D detection dataset that includes gated images captured over 10,000 km of driving data. We validate that our method outperforms state-of-the-art monocular and stereo methods, opening up a new sensor modality as an avenue to replace lidar in autonomous driving. https://light.princeton.edu/gated3d.