Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection

TL;DR

This paper presents a novel stereo 3D object detection framework using only event cameras, leveraging a dual filter mechanism and enhanced regression to improve perception in dynamic environments.

Contribution

It introduces a purely event-camera-based stereo detection method with a dual filter and improved regression, eliminating reliance on traditional sensors.

Findings

Outperforms prior methods in dynamic scenarios

Demonstrates robustness in high-speed environments

Achieves continuous-time 3D perception

Abstract

3D object detection is essential for autonomous systems, enabling precise localization and dimension estimation. While LiDAR and RGB cameras are widely used, their fixed frame rates create perception gaps in high-speed scenarios. Event cameras, with their asynchronous nature and high temporal resolution, offer a solution by capturing motion continuously. The recent approach, which integrates event cameras with conventional sensors for continuous-time detection, struggles in fast-motion scenarios due to its dependency on synchronized sensors. We propose a novel stereo 3D object detection framework that relies solely on event cameras, eliminating the need for conventional 3D sensors. To compensate for the lack of semantic and geometric information in event data, we introduce a dual filter mechanism that extracts both. Additionally, we enhance regression by aligning bounding boxes with object-centric information. Experiments show that our method outperforms prior approaches in dynamic environments, demonstrating the potential of event cameras for robust, continuous-time 3D perception. The code is available at https://github.com/mickeykang16/Ev-Stereo3D.