Efficient Non-Line-of-Sight Imaging from Transient Sinograms

Isogawa, Mariko; Chan, Dorian; Yuan, Ye; O’Toole, Matthew

doi:10.1007/978-3-030-58571-6_12

Cited by 27 publications

(12 citation statements)

References 30 publications

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“…The existing non-line-of-sight imaging systems can rely on the ambient light [4,26,3,36,44] or the active illuminations [16,39,5,29,13,40,21]. To name a few, Bouman et al [4] demonstrate the possibility of using subtle spatialtemporal radiance variations to track the hidden objects behind the wall.…”

Section: Nlos Imaging Systems and Datasetsmentioning

confidence: 99%

“…As a simplification, O'Toole et al [29] propose a new imaging configuration, named confocal NLOS, where the detector and the light source share a common light path. Different from the existing NLOS techniques that densely scan the across the entire relay wall, Isogawa et al [13] design an efficient scanning strategy, called circular confocal NLOS, that reduces both acquisition time and computational requirements. Wu et al [40] introduce a long-range NLOS imaging system by increasing the imaging range from meters to kilometers.…”

Section: Nlos Imaging Systems and Datasetsmentioning

confidence: 99%

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Towards Non-Line-of-Sight Photography

Peng¹,

Mu²,

Nam³

et al. 2021

Preprint

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Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects. Active NLOS imaging systems rely on the capture of the time of flight of light through the scene, and have shown great promise for the accurate and robust reconstruction of hidden scenes without the need for specialized scene setups and prior assumptions. Despite that existing methods can reconstruct 3D geometries of the hidden scene with excellent depth resolution, accurately recovering object textures and appearance with high lateral resolution remains an challenging problem. In this work, we propose a new problem formulation, called NLOS photography, to specifically address this deficiency. Rather than performing an intermediate estimate of the 3D scene geometry, our method follows a data-driven approach and directly reconstructs 2D images of a NLOS scene that closely resemble the pictures taken with a conventional camera from the location of the relay wall. This formulation largely simplifies the challenging reconstruction problem by bypassing the explicit modeling of 3D geometry, and enables the learning of a deep model with a relatively small training dataset. The results are NLOS reconstructions of unprecedented lateral resolution and image quality.

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Section: Nlos Imaging Systems and Datasetsmentioning

confidence: 99%

Section: Nlos Imaging Systems and Datasetsmentioning

confidence: 99%

Towards Non-Line-of-Sight Photography

Peng¹,

Mu²,

Nam³

et al. 2021

Preprint

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“…The relay surface plays a critical role for an NLOS capture system. The LOS portion of transients, as in Equation (8), can be exploited to recover the albedo and the orientation of the relay surface. Specifically, we extract the peak of the histogram 𝜏 LOS (𝑡; s) at each detection point and its corresponding 𝑡, and calculate the depth ℓ of the relay surface.…”

Section: Hardware Alignmentmentioning

confidence: 99%

“…Inspired by a circular scanning pattern in [8], we further present a multi-circle scanning pattern. In a scanning region with the radius 𝑅, we define 𝑁 𝑟 concentric circles, and 𝑁 𝜙 points on each circle.…”

Section: Scanning Patternsmentioning

confidence: 99%

“…The first bounce corresponds to direct reflection and enables us to recover the shape and albedo of the relay surface. By removing or gating the photons from the first bounce, photons from the third bounce are employed to reconstruct [3][4][5][6][7][8][9][10] and localize [5,6,[10][11][12] hidden objects in the NLOS scene. Potential applications are numerous, including autonomous driving, remote sensing, and biomedical imaging.…”

Section: Introductionmentioning

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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