Non-line-of-sight reconstruction with signal–object collaborative regularization

Xintong Liu; Jianyu Wang; Zhupeng Li; Zuoqiang Shi; Xing Fu; Lingyun Qiu

doi:10.1038/s41377-021-00633-3

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Non-line-of-sight reconstruction with signal–object collaborative regularization

Articles

- Non-line-of-sight reconstruction with signal–object collaborative regularization
- Light: Science & Applications Vol. 10, Issue 10, Pages: 1967-1986(2021)
- Affiliations：
  
  1.Yau Mathematical Sciences Center, Tsinghua University, 100084, Beijing, China
  2.State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, 100084, Beijing, China
  3.Key Laboratory of Photonic Control Technology (Tsinghua University), Ministry of Education, 100084, Beijing, China
  4.Department of Mathematical Sciences, Tsinghua University, 100084, Beijing, China
  5.Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, 101408, Beijing, China
- Author bio：
  
  Xing Fu (fuxing@tsinghua.edu.cn)
  Lingyun Qiu (lyqiu@tsinghua.edu.cn)
- Funds：
- DOI：10.1038/s41377-021-00633-3
  CLC：
- Published：31 October 2021，
  
  Published Online：24 September 2021，
  
  Received：25 May 2021，
  
  Revised：18 August 2021，
  
  Accepted：03 September 2021
- Accepted：
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Liu, X. T. et al. Non-line-of-sight reconstruction with signal–object collaborative regularization. Light: Science & Applications, 10, 1967-1986 (2021).
DOI：

Liu, X. T. et al. Non-line-of-sight reconstruction with signal–object collaborative regularization. Light: Science & Applications, 10, 1967-1986 (2021). DOI： 10.1038/s41377-021-00633-3.

摘要

Abstract

Non-line-of-sight imaging aims at recovering obscured objects from multiple scattered lights. It has recently received widespread attention due to its potential applications

such as autonomous driving

rescue operations

and remote sensing. However

in cases with high measurement noise

obtaining high-quality reconstructions remains a challenging task. In this work

we establish a unified regularization framework

which can be tailored for different scenarios

including indoor and outdoor scenes with substantial background noise under both confocal and non-confocal settings. The proposed regularization framework incorporates sparseness and non-local self-similarity of the hidden objects as well as the smoothness of the signals. We show that the estimated signals

albedo

and surface normal of the hidden objects can be reconstructed robustly even with high measurement noise under the proposed framework. Reconstruction results on synthetic and experimental data show that our approach recovers the hidden objects faithfully and outperforms state-of-the-art reconstruction algorithms in terms of both quantitative criteria and visual quality.

关键词

Keywords

references

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