Non-line-of-sight imaging with arbitrary illumination and detection pattern
Abstract Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at regular grid points in a large area of the relay surface, which severely hinders their availability to variable relay scenar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-38898-4 |
| _version_ | 1797811436623233024 |
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| author | Xintong Liu Jianyu Wang Leping Xiao Zuoqiang Shi Xing Fu Lingyun Qiu |
| author_facet | Xintong Liu Jianyu Wang Leping Xiao Zuoqiang Shi Xing Fu Lingyun Qiu |
| author_sort | Xintong Liu |
| collection | DOAJ |
| description | Abstract Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at regular grid points in a large area of the relay surface, which severely hinders their availability to variable relay scenarios in practical applications such as robotic vision, autonomous driving, rescue operations and remote sensing. In this work, we propose a Bayesian framework for NLOS imaging without specific requirements on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we design a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm for high-quality reconstructions. Our approach is capable of reconstructing both the albedo and surface normal of the hidden objects with fine details under general relay settings. Moreover, with a regular relay surface, coarse rather than dense measurements are enough for our approach such that the acquisition time can be reduced significantly. As demonstrated in multiple experiments, the proposed framework substantially extends the application range of NLOS imaging. |
| first_indexed | 2024-03-13T07:23:40Z |
| format | Article |
| id | doaj.art-cc776e359be34f8ba7dfdc7448a07976 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T07:23:40Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-cc776e359be34f8ba7dfdc7448a079762023-06-04T11:32:48ZengNature PortfolioNature Communications2041-17232023-06-0114111210.1038/s41467-023-38898-4Non-line-of-sight imaging with arbitrary illumination and detection patternXintong Liu0Jianyu Wang1Leping Xiao2Zuoqiang Shi3Xing Fu4Lingyun Qiu5Yau Mathematical Sciences Center, Tsinghua UniversityYau Mathematical Sciences Center, Tsinghua UniversityState Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua UniversityYau Mathematical Sciences Center, Tsinghua UniversityState Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua UniversityYau Mathematical Sciences Center, Tsinghua UniversityAbstract Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at regular grid points in a large area of the relay surface, which severely hinders their availability to variable relay scenarios in practical applications such as robotic vision, autonomous driving, rescue operations and remote sensing. In this work, we propose a Bayesian framework for NLOS imaging without specific requirements on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we design a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm for high-quality reconstructions. Our approach is capable of reconstructing both the albedo and surface normal of the hidden objects with fine details under general relay settings. Moreover, with a regular relay surface, coarse rather than dense measurements are enough for our approach such that the acquisition time can be reduced significantly. As demonstrated in multiple experiments, the proposed framework substantially extends the application range of NLOS imaging.https://doi.org/10.1038/s41467-023-38898-4 |
| spellingShingle | Xintong Liu Jianyu Wang Leping Xiao Zuoqiang Shi Xing Fu Lingyun Qiu Non-line-of-sight imaging with arbitrary illumination and detection pattern Nature Communications |
| title | Non-line-of-sight imaging with arbitrary illumination and detection pattern |
| title_full | Non-line-of-sight imaging with arbitrary illumination and detection pattern |
| title_fullStr | Non-line-of-sight imaging with arbitrary illumination and detection pattern |
| title_full_unstemmed | Non-line-of-sight imaging with arbitrary illumination and detection pattern |
| title_short | Non-line-of-sight imaging with arbitrary illumination and detection pattern |
| title_sort | non line of sight imaging with arbitrary illumination and detection pattern |
| url | https://doi.org/10.1038/s41467-023-38898-4 |
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