A dataset for benchmarking time-resolved non-line-of-sight imaging

Villagómez-Galindo, Miguel; Marco, Julio; O’Toole, Matthew; Wetzstein, Gordon; Gutiérrez, Diego; Jarabo, Adrián

doi:10.1145/3306214.3338583

Cited by 27 publications

(17 citation statements)

References 10 publications

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“…Full color NLOS imaging with single pixel photomultiplier tube combined with a mask 23,24 has also been demonstrated. Further work includes real-time transient imaging for amplitude modulated continuous wave lidar applications 25 , analysis of missing features based on time-resolved NLOS measurements 26 , convolutional approximations to incorporate priors into FBP 27 , occlusion-aided NLOS imaging using SPADs 28,29 , Bayesian statistics reconstruction to account for random errors 30 , temporal focusing for a hidden volume of interest by altering the time delay profile of the hardware illumination 31 , and a database for NLOS imaging problems with different acquisition schemes 32 . Reconstruction times for all these methods remain in the minutes to hours range even for small scenes of less than a meter in diameter.…”

Section: Discussionmentioning

confidence: 99%

Phasor field diffraction based reconstruction for fast non-line-of-sight imaging systems

2020

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Non-line-of-sight (NLOS) imaging recovers objects using diffusely reflected indirect light using transient illumination devices in combination with a computational inverse method. While capture systems capable of collecting light from the entire NLOS relay surface can be much more light efficient than single pixel point scanning detection, current reconstruction algorithms for such systems have computational and memory requirements that prevent real-time NLOS imaging. Existing real-time demonstrations also use retroreflective targets and reconstruct at resolutions far below the hardware limits. Our method presented here enables the reconstruction of room-sized scenes from non-confocal, parallel multi-pixel measurements in seconds with less memory usage. We anticipate that our method will enable real-time NLOS imaging when used with emerging single-photon avalanche diode array detectors with resolution only limited by the temporal resolution of the sensor.

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Section: Discussionmentioning

confidence: 99%

Phasor field diffraction based reconstruction for fast non-line-of-sight imaging systems

2020

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“…Finally, an interesting extension would be to NLOS imaging, especially given the latest developments exploiting computational techniques for image information retrieval from temporal data [16,[40][41][42][43] and the availability of public data-sets [44,45].…”

Section: Discussionmentioning

confidence: 99%

Spatial images from temporal data

Turpin

Musarra

Kapitany

et al. 2020

Optica

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Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial structure in the detector or illumination, i.e., imaging with just a single-point sensor, would require solving a very strongly ill-posed inverse retrieval problem that to date has not been solved. Here, we demonstrate a data-driven approach in which full 3D information is obtained with just a single-point, single-photon avalanche diode that records the arrival time of photons reflected from a scene that is illuminated with short pulses of light. Imaging with single-point time-of-flight (temporal) data opens new routes in terms of speed, size, and functionality. As an example, we show how the training based on an optical time-of-flight camera enables a compact radio-frequency impulse radio detection and ranging transceiver to provide 3D images. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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“…We used 200 scenes in the two-object case. For 2D imaging, we used simulated transient data from the Z-NLOS Dataset [9,14], which we resized to 64 × 64 × 2048. For 3D imaging, we test our algorithm on real captured data provided by O'Toole et al [27], as well as simulated data from the Z-NLOS Dataset, all rescaled to 64 × 64 × 512.…”

Section: Methodsmentioning

confidence: 99%

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

Isogawa

Chan

Yuan

et al. 2020

Preprint

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Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e.g., walls) to see around corners. One approach involves using pulsed lasers and ultrafast sensors to measure the travel time of multiply scattered light. Unlike existing NLOS techniques that generally require densely raster scanning points across the entirety of a relay wall, we explore a more efficient form of NLOS scanning that reduces both acquisition times and computational requirements. We propose a circular and confocal non-line-of-sight (C 2 NLOS) scan that involves illuminating and imaging a common point, and scanning this point in a circular path along a wall. We observe that (1) these C 2 NLOS measurements consist of a superposition of sinusoids, which we refer to as a transient sinogram, (2) there exists computationally efficient reconstruction procedures that transform these sinusoidal measurements into 3D positions of hidden scatterers or NLOS images of hidden objects, and (3) despite operating on an order of magnitude fewer measurements than previous approaches, these C 2 NLOS scans provide sufficient information about the hidden scene to solve these different NLOS imaging tasks. We show results from both simulated and real C 2 NLOS scans. 1

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A dataset for benchmarking time-resolved non-line-of-sight imaging

Cited by 27 publications

References 10 publications

Phasor field diffraction based reconstruction for fast non-line-of-sight imaging systems

Phasor field diffraction based reconstruction for fast non-line-of-sight imaging systems

Spatial images from temporal data

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

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