Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues

TL;DR

Gated3D introduces a novel monocular 3D object detection method using temporal illumination cues from gated images, outperforming existing approaches at long distances and offering a cost-effective alternative to lidar.

Contribution

The paper presents a new deep detector architecture, Gated3D, leveraging gated imagery for improved 3D detection in autonomous driving.

Findings

Outperforms state-of-the-art monocular and stereo methods at long ranges

Uses a novel dataset with gated images from extensive driving data

Demonstrates the potential of gated imaging as a lidar alternative

Abstract

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.