Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras

TL;DR

This paper introduces Ev-3DOD, a novel approach using event cameras for high-speed 3D object detection in autonomous driving, addressing latency issues of traditional sensors and establishing a new benchmark dataset.

Contribution

It pioneers the integration of event cameras into 3D object detection and provides the first dataset and benchmark for this technology.

Findings

Enables detection during inter-frame intervals

Achieves high temporal resolution at 100 FPS

Provides a new dataset for event-based 3D detection

Abstract

Detecting 3D objects in point clouds plays a crucial role in autonomous driving systems. Recently, advanced multi-modal methods incorporating camera information have achieved notable performance. For a safe and effective autonomous driving system, algorithms that excel not only in accuracy but also in speed and low latency are essential. However, existing algorithms fail to meet these requirements due to the latency and bandwidth limitations of fixed frame rate sensors, e.g., LiDAR and camera. To address this limitation, we introduce asynchronous event cameras into 3D object detection for the first time. We leverage their high temporal resolution and low bandwidth to enable high-speed 3D object detection. Our method enables detection even during inter-frame intervals when synchronized data is unavailable, by retrieving previous 3D information through the event camera. Furthermore, we introduce the first event-based 3D object detection dataset, DSEC-3DOD, which includes ground-truth 3D bounding boxes at 100 FPS, establishing the first benchmark for event-based 3D detectors. The code and dataset are available at https://github.com/mickeykang16/Ev3DOD.