Passive Non-line-of-sight Imaging for Moving Targets with an Event Camera

TL;DR

This paper introduces a novel event-based passive NLOS imaging technique that effectively captures moving targets behind obstacles, outperforming traditional frame-based methods in quality and efficiency.

Contribution

The paper presents the first event-based NLOS imaging method and dataset, improving dynamic target recognition and reducing data volume compared to existing approaches.

Findings

Event-based method outperforms frame-based in PSNR and LPIPS by 20% and 10%.

Event data volume is only 2% of traditional methods.

Created the first dataset for event-based NLOS imaging.

Abstract

Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners. Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods, which show limitations in recognition of moving targets. To the best of our knowledge, we propose a novel event-based passive NLOS imaging method. We acquire asynchronous event-based data which contains detailed dynamic information of the NLOS target, and efficiently ease the degradation of speckle caused by movement. Besides, we create the first event-based NLOS imaging dataset, NLOS-ES, and the event-based feature is extracted by time-surface representation. We compare the reconstructions through event-based data with frame-based data. The event-based method performs well on PSNR and LPIPS, which is 20% and 10% better than frame-based method, while the data volume takes only 2% of traditional method.