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

Geng, Ruixu; Yang, Hu; Chen, Yan

doi:10.48550/arxiv.2104.13807

Cited by 3 publications

(3 citation statements)

References 92 publications

(258 reference statements)

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“…Streak cameras and SPADs have high resolution of several picoseconds, but the former are bulky and incompatible with most capture systems in practice. For more details of time-resolved detectors, we refer readers to recent reviews [1,2,30].…”

Section: Related Workmentioning

confidence: 99%

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Onsite Non-Line-of-Sight Imaging via Online Calibrations

Pan¹,

Li²,

Gao³

et al. 2021

Preprint

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There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle. Existing solutions generally pre-calibrate the system before scanning the hidden objects. Onsite adjustments of the occluder, object and scanning pattern require re-calibration. We present an online calibration technique that directly decouples the acquired transients at onsite scanning into the LOS and hidden components. We use the former to directly (re)calibrate the system upon changes of scene/obstacle configurations, scanning regions, and scanning patterns whereas the latter for hidden object recovery via spatial, frequency or learning based techniques. Our technique avoids using auxiliary calibration apparatus such as mirrors or checkerboards and supports both laboratory validations and realworld deployments.

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Section: Related Workmentioning

confidence: 99%

“…Non-line-of-sight (NLOS) imaging aims at recovering objects outside the direct line of sight of a sensor [1,2]. Most active NLOS capture systems exploit an ultra-fast pulsed laser beam that can be controlled to direct toward a relay surface (e.g., a wall).…”

Section: Introductionmentioning

confidence: 99%

Onsite Non-Line-of-Sight Imaging via Online Calibrations

Pan¹,

Li²,

Gao³

et al. 2021

Preprint

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show abstract

“…For high-accuracy measurements, the entire setup of the system needs to be calibrated such that the geometric deployment of devices and hidden objects is known, which results in reliable tasks such as reconstruction and tracking of the hidden objects. Here, we briefly review the most relevant prior work and refer readers to recent surveys [1], [2], [22] for a comprehensive overview.…”

mentioning

confidence: 99%

Onsite Non-Line-of-Sight Imaging via Online Calibration

Pan

Gao

et al. 2022

IEEE Photonics J.

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There has been increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects hidden behind corners. Existing solutions need to calibrate the imaging system using auxiliary apparatus and additional detectors. We present an online calibration technique that directly decouples the transients, which are acquired by onsite scanning on a relay surface, into line-of-sight (LOS) and hidden components. We use the former to directly (re-)calibrate the system upon changes of scene−surface configurations, scannable regions, and sampling patterns, and the latter for hidden object recovery via spatial-, frequency-, or learning-based techniques. We also calculate a Gamma map from the LOS component to preview calibration effects for accurate transient measurements.The entire process of our calibration for 64 scanning points takes no more than 14 seconds on an Intel i7-6600H CPU. In particular, our technique avoids using auxiliary calibration tools such as mirrors or checkerboards and supports both uniform and non-uniform sampling in an onsite NLOS imaging system. Comprehensive experiments via calibration evaluation and NLOS reconstruction demonstrate the efficiency and effectiveness of our solution. Besides, we have made our data and code open-source on GitHub to the research community.

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High-resolution non-confocal non-line-of-sight imaging based on spherical-slice transform from spatial and temporal frequency to space and time

Yu,

Xie,

Yang

et al. 2024

Opt. Lett.

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Current non-confocal non-line-of-sight (NLOS) imaging faces the problems of low resolution and limited scene adaptability. We propose a non-confocal NLOS imaging method based on spherical-slice transform from spatial and temporal frequency to space and time. Simulation and experimental results show that the proposed method has high-resolution reconstruction without artifact interference, shape distortion, and position offset. Furthermore, it has strong scene adaptability. After GPU acceleration, the reconstruction time of the proposed method can be reduced to several hundred milliseconds for the PF32 photon array camera with 32 × 32 detection units. In the future, the proposed method has great potential for application in real-time NLOS imaging systems.

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

Cited by 3 publications

References 92 publications

Onsite Non-Line-of-Sight Imaging via Online Calibrations

Onsite Non-Line-of-Sight Imaging via Online Calibrations

Onsite Non-Line-of-Sight Imaging via Online Calibration

High-resolution non-confocal non-line-of-sight imaging based on spherical-slice transform from spatial and temporal frequency to space and time

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