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

Xintong Liu Yau Mathematical Sciences Center, Tsinghua University, 100084 Beijing, China Jianyu Wang Yau Mathematical Sciences Center, Tsinghua University, 100084 Beijing, China Zhupeng Li State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, 100084, Beijing, China; Key Laboratory of Photonic Control Technology (Tsinghua University), Ministry of Education, 100084, Beijing, China Zuoqiang Shi Department of Mathematical Sciences, Tsinghua University, 100084, Beijing, China; Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, 101408, Beijing, China Xing Fu State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, 100084, Beijing, China; Key Laboratory of Photonic Control Technology (Tsinghua University), Ministry of Education, 100084, Beijing, China Lingyun Qiu Yau Mathematical Sciences Center, Tsinghua University, 100084, Beijing, China; Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, 101408, Beijing, China

TBD mathscidoc:2204.43013

Light: Science & Applications, 10, 2021.9
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.
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@inproceedings{xintong2021non-line-of-sight,
  title={Non-line-of-sight reconstruction with signal–object collaborative regularization},
  author={Xintong Liu, Jianyu Wang, Zhupeng Li, Zuoqiang Shi, Xing Fu, and Lingyun Qiu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422154940864416111},
  booktitle={Light: Science & Applications},
  volume={10},
  year={2021},
}
Xintong Liu, Jianyu Wang, Zhupeng Li, Zuoqiang Shi, Xing Fu, and Lingyun Qiu. Non-line-of-sight reconstruction with signal–object collaborative regularization. 2021. Vol. 10. In Light: Science & Applications. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422154940864416111.
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