APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 1

Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes

Ruixu Geng, School of Information and Communication Engineering, University of Electronic Science and Technology of China, China, Yang Hu, School of Information Science and Technology, University of Science and Technology of China, China, eeyhu@ustc.edu.cn , Yan Chen, School of Cyber Science and Technology, University of Science and Technology of China, China
 
Suggested Citation
Ruixu Geng, Yang Hu and Yan Chen (2022), "Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 1, e1. http://dx.doi.org/10.1561/116.00000019

Publication Date: 21 Feb 2022
© 2022 R. Geng, Y. Hu and Y. Chen
 
Subjects
 
Keywords
Non-line-of-sight (NLOS)deep learningactive NLOS imagingpassive NLOS imaging
 

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In this article:
Introduction 
Active Methods 
Passive Methods 
Deep Learning Methods 
New NLOS Scenes 
Conclusion 
References 

Abstract

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.

DOI:10.1561/116.00000019