As an emerging technology that has attracted huge attention, non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface, with broad application prospects in the fields of autonomous driving, medical imaging, and defense. Despite the challenges of low signal-to-noise ratio (SNR) and high ill-posedness, NLOS imaging has been developed rapidly in recent years. Most current NLOS imaging technologies use conventional physical models, constructing imaging models through active or passive illumination and using reconstruction algorithms to restore hidden scenes. Moreover, deep learning algorithms for NLOS imaging have also received much attention recently. This paper presents a comprehensive overview of both conventional and deep learning-based NLOS imaging techniques. Besides, we also survey new proposed NLOS scenes, and discuss the challenges and prospects of existing technologies. Such a survey can help readers have an overview of different types of NLOS imaging, thus expediting the development of seeing around corners.
翻译:作为引起极大关注的新兴技术,非视觉成像可以通过分析中继表面的弥漫反射,在自主驾驶、医疗成像和防御领域广泛应用前景,重建隐藏物体。尽管信号对噪音比率低和高度不良的挑战,但近些年来还迅速开发了NLOS成像。大多数目前的NLOS成像技术使用常规物理模型,通过主动或被动的照明建立成像模型,并利用重建算法恢复隐藏的场景。此外,NLOS成像的深学习算法最近也受到极大关注。本文还全面概述了常规和深入学习的NLOS成像技术。此外,我们还调查了NLOS的新场景,并讨论了现有技术的挑战和前景。这种调查有助于读者对NLOS成像的不同类型进行概览,从而加快了在角落的视觉发展。