Literature DB >> 33658383

Non-line-of-sight imaging over 1.43 km.

Cheng Wu1,2,3,4, Jianjiang Liu1,2,3,4,5, Xin Huang1,2,3,4, Zheng-Ping Li1,2,3,4, Chao Yu1,2,3,4, Jun-Tian Ye1,2,3,4, Jun Zhang1,2,3,4, Qiang Zhang1,2,3,4, Xiankang Dou1,2,5,6, Vivek K Goyal7, Feihu Xu8,2,3,4, Jian-Wei Pan8,2,3,4.   

Abstract

Non-line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial-temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial-temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.
Copyright © 2021 the Author(s). Published by PNAS.

Entities:  

Keywords:  computational imaging; computer vision; non–line-of-sight imaging; optical imaging

Year:  2021        PMID: 33658383     DOI: 10.1073/pnas.2024468118

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  2 in total

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Authors:  Xiaohua Feng; Yayao Ma; Liang Gao
Journal:  Nat Commun       Date:  2022-06-09       Impact factor: 17.694

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Authors:  Tian Shi; Liangsheng Li; He Cai; Xianli Zhu; Qingfan Shi; Ning Zheng
Journal:  Nat Commun       Date:  2022-07-14       Impact factor: 17.694

  2 in total

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