Abstract

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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  1. J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008).
    [Crossref]
  2. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
    [Crossref]
  3. M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
    [Crossref]
  4. D. B. Lindell, M. O’Toole, and G. Wetzstein, “Single-photon 3D imaging with deep sensor fusion,” ACM Trans. Graph. 37, 1–12 (2018).
    [Crossref]
  5. N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
    [Crossref]
  6. C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.
  7. S. T. Barnard and M. A. Fischler, “Computational stereo,” ACM Comput. Surv. 14, 553–572 (1982).
    [Crossref]
  8. Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
    [Crossref]
  9. B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
    [Crossref]
  10. M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
    [Crossref]
  11. P. Dong and Q. Chen, LiDAR Remote Sensing and Applications (CRC Press, 2017).
  12. A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
    [Crossref]
  13. P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
    [Crossref]
  14. J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
    [Crossref]
  15. A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
    [Crossref]
  16. G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
    [Crossref]
  17. M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
    [Crossref]
  18. C. Jin, J. Xie, S. Zhang, Z. Zhang, and Y. Zhao, “Reconstruction of multiple non-line-of-sight objects using back projection based on ellipsoid mode decomposition,” Opt. Express 26, 20089–20101 (2018).
    [Crossref]
  19. V. Arellano, D. Gutierrez, and A. Jarabo, “Fast back-projection for non-line of sight reconstruction,” Opt. Express 25, 11574–11583 (2017).
    [Crossref]
  20. G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
    [Crossref]
  21. Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
    [Crossref]
  22. M. I. Jordan and T. M. Mitchell, “Machine learning: trends, perspectives, and prospects,” Science 349, 255–260 (2015).
    [Crossref]
  23. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521,436–444 (2015).
    [Crossref]
  24. G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6, 921–943 (2019).
    [Crossref]
  25. L. Waller and L. Tian, “Computational imaging: machine learning for 3D microscopy,” Nature 523, 416–417 (2015).
    [Crossref]
  26. Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
    [Crossref]
  27. E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-storm: super-resolution single-molecule microscopy by deep learning,” Optica 5, 458–464 (2018).
    [Crossref]
  28. H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
    [Crossref]
  29. Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
    [Crossref]
  30. A. Goy, K. Arthur, S. Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121, 243902 (2018).
    [Crossref]
  31. A. Sinha, J. Lee, S. Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4, 1117–1125 (2017).
    [Crossref]
  32. Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
    [Crossref]
  33. S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5, 803–813 (2018).
    [Crossref]
  34. N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, “Learning to see through multimode fibers,” Optica 5, 960–966 (2018).
    [Crossref]
  35. Y. Li, Y. Xue, and L. Tian, “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5, 1181–1190 (2018).
    [Crossref]
  36. A. Turpin, I. Vishniakou, and J. D. Seelig, “Light scattering control in transmission and reflection with neural networks,” Opt. Express 26, 30911–30929 (2018).
    [Crossref]
  37. B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
    [Crossref]
  38. P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
    [Crossref]
  39. P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
    [Crossref]
  40. D. Faccio, A. Velten, and G. Wetzstein, “Non-line-of-sight imaging,” Nat. Rev. Phys. 2, 318–327 (2020).
    [Crossref]
  41. M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
    [Crossref]
  42. X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
    [Crossref]
  43. J. Iseringhausen and M. B. Hullin, “Non-line-of-sight reconstruction using efficient transient rendering,” ACM Trans. Graph. 39, 1–14 (2020).
    [Crossref]
  44. M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.
  45. J. Klein, M. Laurenzis, D. L. Michels, and M. B. Hullin, “NLoS Benchmark” (2019).

2020 (2)

D. Faccio, A. Velten, and G. Wetzstein, “Non-line-of-sight imaging,” Nat. Rev. Phys. 2, 318–327 (2020).
[Crossref]

J. Iseringhausen and M. B. Hullin, “Non-line-of-sight reconstruction using efficient transient rendering,” ACM Trans. Graph. 39, 1–14 (2020).
[Crossref]

2019 (8)

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
[Crossref]

M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
[Crossref]

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6, 921–943 (2019).
[Crossref]

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

2018 (15)

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

A. Goy, K. Arthur, S. Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121, 243902 (2018).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5, 803–813 (2018).
[Crossref]

N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, “Learning to see through multimode fibers,” Optica 5, 960–966 (2018).
[Crossref]

Y. Li, Y. Xue, and L. Tian, “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5, 1181–1190 (2018).
[Crossref]

A. Turpin, I. Vishniakou, and J. D. Seelig, “Light scattering control in transmission and reflection with neural networks,” Opt. Express 26, 30911–30929 (2018).
[Crossref]

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-storm: super-resolution single-molecule microscopy by deep learning,” Optica 5, 458–464 (2018).
[Crossref]

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

C. Jin, J. Xie, S. Zhang, Z. Zhang, and Y. Zhao, “Reconstruction of multiple non-line-of-sight objects using back projection based on ellipsoid mode decomposition,” Opt. Express 26, 20089–20101 (2018).
[Crossref]

D. B. Lindell, M. O’Toole, and G. Wetzstein, “Single-photon 3D imaging with deep sensor fusion,” ACM Trans. Graph. 37, 1–12 (2018).
[Crossref]

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

2017 (3)

2016 (2)

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

2015 (4)

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

L. Waller and L. Tian, “Computational imaging: machine learning for 3D microscopy,” Nature 523, 416–417 (2015).
[Crossref]

M. I. Jordan and T. M. Mitchell, “Machine learning: trends, perspectives, and prospects,” Science 349, 255–260 (2015).
[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521,436–444 (2015).
[Crossref]

2014 (1)

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

2013 (1)

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

2012 (1)

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

2008 (2)

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008).
[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

2006 (1)

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

1982 (1)

S. T. Barnard and M. A. Fischler, “Computational stereo,” ACM Comput. Surv. 14, 553–572 (1982).
[Crossref]

Altmann, Y.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

Arellano, V.

Arthur, K.

A. Goy, K. Arthur, S. Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121, 243902 (2018).
[Crossref]

Aspden, R. S.

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

Baraniuk, R. G.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Barbastathis, G.

Barnard, S. T.

S. T. Barnard and M. A. Fischler, “Computational stereo,” ACM Comput. Surv. 14, 553–572 (1982).
[Crossref]

Bawendi, M. G.

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Bell, J. E. C.

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521,436–444 (2015).
[Crossref]

Bentolila, L. A.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Boccolini, A.

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Borhani, N.

Bowman, A.

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Bowman, R.

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Boyd, R. W.

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

Buller, S.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

Buschek, D.

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Callenberg, C.

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

Caramazza, P.

P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
[Crossref]

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Ceylan Koydemir, H.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Chen, Q.

P. Dong and Q. Chen, LiDAR Remote Sensing and Applications (CRC Press, 2017).

Colaco, A.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Conca, E.

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Davenport, M. A.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

den Brok, D.

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

Deng, M.

Dong, P.

P. Dong and Q. Chen, LiDAR Remote Sensing and Applications (CRC Press, 2017).

Duarte, M. F.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Edgar, M. P.

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Faccio, D.

D. Faccio, A. Velten, and G. Wetzstein, “Non-line-of-sight imaging,” Nat. Rev. Phys. 2, 318–327 (2020).
[Crossref]

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
[Crossref]

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

Fischler, M. A.

S. T. Barnard and M. A. Fischler, “Computational stereo,” ACM Comput. Surv. 14, 553–572 (1982).
[Crossref]

Frauel, Y.

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

Galindo, M.

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

Gao, R.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Gariepy, G.

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

Gerald, J.-Y. T.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

Gibson, G. M.

M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

Gorocs, Z.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Göröcs, Z.

Goy, A.

A. Goy, K. Arthur, S. Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121, 243902 (2018).
[Crossref]

Goyal, V. K.

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Guillén, I.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Günaydin, H.

Gunaydn, H.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Günaydn, H.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

Gupta, O.

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Gutierrez, D.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

V. Arellano, D. Gutierrez, and A. Jarabo, “Fast back-projection for non-line of sight reconstruction,” Opt. Express 25, 11574–11583 (2017).
[Crossref]

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

Henderson, R.

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

Hero, A. O.

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

Higham, C. F.

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521,436–444 (2015).
[Crossref]

Hullin, M.

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Hullin, M. B.

J. Iseringhausen and M. B. Hullin, “Non-line-of-sight reconstruction using efficient transient rendering,” ACM Trans. Graph. 39, 1–14 (2020).
[Crossref]

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

J. Klein, M. Laurenzis, D. L. Michels, and M. B. Hullin, “NLoS Benchmark” (2019).

Iseringhausen, J.

J. Iseringhausen and M. B. Hullin, “Non-line-of-sight reconstruction using efficient transient rendering,” ACM Trans. Graph. 39, 1–14 (2020).
[Crossref]

Jarabo, A.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

V. Arellano, D. Gutierrez, and A. Jarabo, “Fast back-projection for non-line of sight reconstruction,” Opt. Express 25, 11574–11583 (2017).
[Crossref]

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

Javidi, B.

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

Jin, C.

Jin, Y.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Jordan, M. I.

M. I. Jordan and T. M. Mitchell, “Machine learning: trends, perspectives, and prospects,” Science 349, 255–260 (2015).
[Crossref]

Kakkava, E.

Kelly, K. F.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Kirmani, A.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Klein, J.

J. Klein, M. Laurenzis, D. L. Michels, and M. B. Hullin, “NLoS Benchmark” (2019).

Konstantinou, G.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

Kural, C.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

La Manna, M.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Lamb, R.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

Laska, J. N.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Laurenzis, M.

J. Klein, M. Laurenzis, D. L. Michels, and M. B. Hullin, “NLoS Benchmark” (2019).

Le, T. H.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Leach, J.

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521,436–444 (2015).
[Crossref]

Lee, J.

Li, S.

Li, Y.

Liang, K.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Lindell, D. B.

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

D. B. Lindell, M. O’Toole, and G. Wetzstein, “Single-photon 3D imaging with deep sensor fusion,” ACM Trans. Graph. 37, 1–12 (2018).
[Crossref]

Liu, X.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Loterie, D.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

Lyons, A.

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

Marco, J.

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

Matoba, O.

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

McCarthy, A.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

McLaughlin, S.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

Mellado, N.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

Mertens, L.

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

Michaeli, T.

Michels, D. L.

J. Klein, M. Laurenzis, D. L. Michels, and M. B. Hullin, “NLoS Benchmark” (2019).

Mitchell, T. M.

M. I. Jordan and T. M. Mitchell, “Machine learning: trends, perspectives, and prospects,” Science 349, 255–260 (2015).
[Crossref]

Moran, O.

P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
[Crossref]

Morris, P. A.

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

Moser, C.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, “Learning to see through multimode fibers,” Optica 5, 960–966 (2018).
[Crossref]

Murray-Smith, R.

P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
[Crossref]

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Musarra, G.

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Nam, J. H.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Naughton, T. J.

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

Nehme, E.

O’Toole, M.

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

D. B. Lindell, M. O’Toole, and G. Wetzstein, “Single-photon 3D imaging with deep sensor fusion,” ACM Trans. Graph. 37, 1–12 (2018).
[Crossref]

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

Ozcan, A.

G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6, 921–943 (2019).
[Crossref]

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]

Padgett, M.

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Padgett, M. J.

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
[Crossref]

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Psaltis, D.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, “Learning to see through multimode fibers,” Optica 5, 960–966 (2018).
[Crossref]

Radwell, N.

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

Rahmani, B.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

Raskar, R.

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Ren, Z.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Reza, S. A.

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Rivenson, Y.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]

Seelig, J. D.

Selyem, A.

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

Shapiro, J. H.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008).
[Crossref]

Shechtman, Y.

Shin, D.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Sinha, A.

Situ, G.

Sun, B.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Sun, M.-J.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

Sun, T.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Tachella, J.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

Tajahuerce, E.

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

Takhar, D.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Teng, D.

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

Tian, L.

Tobin, R.

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

Tonolini, F.

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

Tseng, D.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Turpin, A.

Veeraraghavan, A.

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Velten, A.

D. Faccio, A. Velten, and G. Wetzstein, “Non-line-of-sight imaging,” Nat. Rev. Phys. 2, 318–327 (2020).
[Crossref]

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Venkatraman, D.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Villa, F.

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Vishniakou, I.

Vittert, L. E.

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Waller, L.

L. Waller and L. Tian, “Computational imaging: machine learning for 3D microscopy,” Nature 523, 416–417 (2015).
[Crossref]

Wang, H.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]

Wei, Z.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Weiss, L. E.

Welsh, S.

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Wetzstein, G.

D. Faccio, A. Velten, and G. Wetzstein, “Non-line-of-sight imaging,” Nat. Rev. Phys. 2, 318–327 (2020).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

D. B. Lindell, M. O’Toole, and G. Wetzstein, “Single-photon 3D imaging with deep sensor fusion,” ACM Trans. Graph. 37, 1–12 (2018).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

Willwacher, T.

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Wong, F. N. C.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Xie, J.

Xue, Y.

Zappa, F.

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Zhang, S.

Zhang, Y.

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]

Zhang, Z.

Zhao, Y.

ACM Comput. Surv. (1)

S. T. Barnard and M. A. Fischler, “Computational stereo,” ACM Comput. Surv. 14, 553–572 (1982).
[Crossref]

ACM Trans. Graph. (2)

D. B. Lindell, M. O’Toole, and G. Wetzstein, “Single-photon 3D imaging with deep sensor fusion,” ACM Trans. Graph. 37, 1–12 (2018).
[Crossref]

J. Iseringhausen and M. B. Hullin, “Non-line-of-sight reconstruction using efficient transient rendering,” ACM Trans. Graph. 39, 1–14 (2020).
[Crossref]

ACS Photon. (1)

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydn, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photon. 5, 2354–2364 (2018).
[Crossref]

IEEE Signal Process. Mag. (1)

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Light. Sci. Appl. (2)

Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light. Sci. Appl. 7, 17141 (2018).
[Crossref]

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light. Sci. Appl. 7, 69 (2018).
[Crossref]

Nat. Commun. (5)

P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, “Transmission of natural scene images through a multimode fibre,” Nat. Commun. 10, 2029 (2019).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
[Crossref]

P. A. Morris, R. S. Aspden, J. E. C. Bell, R. W. Boyd, and M. J. Padgett, “Imaging with a small number of photons,” Nat. Commun. 6, 5913 (2015).
[Crossref]

J. Tachella, Y. Altmann, N. Mellado, A. McCarthy, R. Tobin, J.-Y. T. Gerald, S. Buller, and S. McLaughlin, “Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers,” Nat. Commun. 10, 4984 (2019).
[Crossref]

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
[Crossref]

Nat. Methods (1)

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydn, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16, 103–110 (2019).
[Crossref]

Nat. Photonics (2)

G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, “Detection and tracking of moving objects hidden from view,” Nat. Photonics 10, 23–26 (2016).
[Crossref]

M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
[Crossref]

Nat. Rev. Phys. (1)

D. Faccio, A. Velten, and G. Wetzstein, “Non-line-of-sight imaging,” Nat. Rev. Phys. 2, 318–327 (2020).
[Crossref]

Nature (5)

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, and A. Velten, “Non-line-of-sight imaging using phasor-field virtual wave optics,” Nature 572, 620–623 (2019).
[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521,436–444 (2015).
[Crossref]

L. Waller and L. Tian, “Computational imaging: machine learning for 3D microscopy,” Nature 523, 416–417 (2015).
[Crossref]

M. O’Toole, D. B. Lindell, and G. Wetzstein, “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature 555, 338–341 (2018).
[Crossref]

Opt. Express (3)

Optica (7)

Phys. Rev. A (1)

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008).
[Crossref]

Phys. Rev. Appl. (1)

G. Musarra, A. Lyons, E. Conca, Y. Altmann, F. Villa, F. Zappa, M. Padgett, and D. Faccio, “Non-line-of-sight 3D imaging with a single-pixel camera,” Phys. Rev. Appl. 12, 011002 (2019).
[Crossref]

Phys. Rev. Lett. (1)

A. Goy, K. Arthur, S. Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121, 243902 (2018).
[Crossref]

Proc. IEEE (1)

Y. Frauel, T. J. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three-dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006).
[Crossref]

Sci. Rep. (2)

N. Radwell, A. Selyem, L. Mertens, M. P. Edgar, and M. J. Padgett, “Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR,” Sci. Rep. 9, 5241 (2019).
[Crossref]

P. Caramazza, A. Boccolini, D. Buschek, M. Hullin, C. F. Higham, R. Henderson, R. Murray-Smith, and D. Faccio, “Neural network identification of people hidden from view with a single-pixel, single-photon detector,” Sci. Rep. 8, 11945 (2018).
[Crossref]

Science (4)

A. Kirmani, D. Venkatraman, D. Shin, A. Colaco, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
[Crossref]

Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum-inspired computational imaging,” Science 361, eaat2298 (2018).
[Crossref]

M. I. Jordan and T. M. Mitchell, “Machine learning: trends, perspectives, and prospects,” Science 349, 255–260 (2015).
[Crossref]

Other (4)

P. Dong and Q. Chen, LiDAR Remote Sensing and Applications (CRC Press, 2017).

C. Callenberg, A. Lyons, D. den Brok, R. Henderson, M. B. Hullin, and D. Faccio, “EMCCD-SPAD camera data fusion for high spatial resolution time-of-flight imaging,” in Computational Optical Sensing and Imaging (Optical Society of America, 2019), paper CTh2A–3.

M. Galindo, J. Marco, M. O’Toole, G. Wetzstein, D. Gutierrez, and A. Jarabo, “A dataset for benchmarking time-resolved non-line-of-sight imaging,” in ACM SIGGRAPH 2019 Posters (2019), pp. 1–2.

J. Klein, M. Laurenzis, D. L. Michels, and M. B. Hullin, “NLoS Benchmark” (2019).

Supplementary Material (3)

NameDescription
» Supplement 1       Supplementary document
» Visualization 1       Performance of our approach for spatial imaging from temporal data gathered with an optical sensor
» Visualization 2       Performance of our approach for spatial imaging from temporal data gathered with a RADAR transducer

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Figures (3)

Fig. 1.
Fig. 1. 3D imaging with single-point time-resolving sensors. Our approach is divided into two steps: (a) a data collection step and (b) the deployment phase. During step 1, a pulsed laser beam flash-illuminates the scene, and the reflected light is collected with a single-point sensor (in our case, SPAD) that provides a temporal histogram via time-correlated single-photon counting (TCSPC). In parallel, a time-of-flight (ToF) camera records 3D images from the scene. The ToF camera operates independently from the SPAD and pulsed laser system. The SPAD temporal histograms and ToF 3D images are used to train the image retrieval ANN. Step 2 occurs only after the ANN is trained. During this deployment phase, only the pulsed laser source and SPAD are used: 3D images are retrieved from the temporal histograms alone.
Fig. 2.
Fig. 2. Numerical results showing 3D imaging from a single temporal histogram recorded with a single-point time-resolving detector. (a) Temporal trace obtained from the scene [shown in (c) as a color-encoded depth image]. (b) 3D image obtained from our image retrieval algorithm when fed with the histogram from (a). The color bars describe the color-encoded depth map.
Fig. 3.
Fig. 3. Experimental results showing the performance of our system recovering the 3D image from temporal histograms in different scenarios. The first column shows temporal histograms recorded with the SPAD sensor and TCSPC electronics [rows (a)–(d)] or with the RADAR transceiver [row (e)], while the last column represents 3D images measured directly with the ToF camera for comparison to the reconstructed images (second column). The color bars describe the color-encoded depth map. The white scale bar corresponds to 80 cm at 2 m distance. Full videos are available in the supplementary information (Visualization 1 and Visualization 2).

Equations (1)

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δ ( d , Δ t ) = c Δ t 2 d c Δ t + 1 ,