Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues
This work addresses the limitations of existing monocular and stereo 3D object detection methods, particularly in low-light or low-contrast conditions, for autonomous driving applications.
The paper introduces a novel 3D object detection modality using a low-cost monocular gated imager and a deep detector architecture called Gated3D. This method leverages temporal illumination cues from three gated images to outperform state-of-the-art monocular and stereo approaches at long distances.
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.