Non-line-of-sight imaging

Abstract

Emerging single-photon-sensitive sensors produce picosecond-accurate time-stamped photon counts. Applying advanced inverse methods to process these data has resulted in unprecedented imaging capabilities, such as non-line-of-sight (NLOS) imaging. Rather than imaging photons that travel along direct paths from a source to an object and back to the detector, NLOS methods analyse photons that travel along indirect light paths, scattered from multiple surfaces, to estimate 3D images of scenes outside the direct line of sight of a camera, hidden by a wall or other obstacles. We review the transient imaging techniques that underlie many NLOS imaging approaches, discuss methods for reconstructing hidden scenes from time-resolved measurements, describe some other methods for NLOS imaging that do not require transient imaging and discuss the future of ‘seeing around corners’.

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Fig. 1: Layout of time-resolved non-line-of-sight imaging.
Fig. 2: First experimental demonstration of ‘looking around corners’.
Fig. 3: Demonstration of the capability of recording light in flight at picosecond timescales for a pulse of light propagating between three mirrors.
Fig. 4: NLOS reconstructions of a hidden room-sized scene.
Fig. 5: Reconstructions of a large scene using the phasor-field virtual wave approach.
Fig. 6: Main detector technologies classified based on spatial and temporal resolution.

References

  1. 1.

    Kirmani, A., Hutchison, T., Davis, J. & Raskar, R. Looking around the corner using transient imaging. Proc. IEEE Int. Conf. Comput. Vis. 2009, 159–166 (2009).

  2. 2.

    Velten, A. et al. Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging. Nat. Commun. 3, 745 (2012). Report of full 3D NLOS imaging.

    ADS  Google Scholar 

  3. 3.

    O’Toole, M., Lindell, D. B. & Wetzstein, G. Confocal non-line-of-sight imaging based on the light-cone transform. Nature 555, 338 (2018). Report applying light-cone transform and producing high-quality 3D reconstructions over a large NLOS area.

    ADS  Google Scholar 

  4. 4.

    Liu, X. et al. Non-line-of-sight imaging using phasor-field virtual wave optics. Nature 572, 620–623 (2019). Virtual-wave reconstruction approach for fast and detailed NLOS scene reconstruction.

    ADS  Google Scholar 

  5. 5.

    Lindell, D. B., Wetzstein, G. & O’Toole, M. Wave-based non-line-of-sight imaging using fast f–k migration. ACM Trans. Graph. 38, 116 (2019). A fast and accurate wave-optics method for 3D NLOS imaging at interactive frame-rates.

    Google Scholar 

  6. 6.

    Bertolotti, J. et al. Non-invasive imaging through opaque scattering layers. Nature 491, 232–234 (2012).

    ADS  Google Scholar 

  7. 7.

    Katz, O., Heidmann, P., Fink, M. & Gigan, S. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photonics 8, 784–790 (2014). Correlation technique for diffuse and NLOS imaging that does not rely on time-of-flight measurements.

  8. 8.

    Lindell, D. B., Wetzstein, G. & Koltun, V. Acoustic non-line-of-sight imaging. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2019, 6773–6782 (2019).

  9. 9.

    Bouman, K. L. et al. Turning corners into cameras: principles and methods. Proc. IEEE Int. Conf. Comput. Vis. 2017, 2289–2297 (2017).

  10. 10.

    Saunders, C., Murray-Bruce, J. & Goyal, V. K. Computational periscopy with an ordinary digital camera. Nature 565, 472–475 (2019). A passive NLOS approach that relies on lighting in the scene and uses ordinary cameras.

  11. 11.

    Boger-Lombard, J. & Katz, O. Non line-of-sight localization by passive optical time-of-flight. Nat. Commun. 10, 3343 (2019).

  12. 12.

    Maeda, T., Wang, Y., Raskar, R. & Kadambi, A. Thermal non-line-of-sight imaging. Proc. IEEE Int. Conf. Comput. Photogr. 2019, 1–11 (2019).

  13. 13.

    Kaga, M. et al. Thermal non-line-of-sight imaging from specular and diffuse reflections. IPSJ Trans. Comp. Vis. Appl. 11, 8 (2019).

  14. 14.

    Faccio, D. & Velten, A. A trillion frames per second: the techniques and applications of light-in-flight photography. Rep. Prog. Phys. 81, 105901 (2018). Review paper on transient imaging.

  15. 15.

    Hariharan, P. Basics of Holography (Cambridge Univ. Press, 2011).

  16. 16.

    Abramson, N. Light-in-flight recording by holography. Opt. Lett. 3, 121 (1978).

    ADS  Google Scholar 

  17. 17.

    Abramson, N. Light-in-flight recording: high-speed holographic motion pictures of ultrafast phenomena. Appl. Opt. 22, 215–232 (1983).

    ADS  Google Scholar 

  18. 18.

    Abramson, N. Light-in-flight recording. 4: Visualizing optical relativistic phenomena. Appl. Opt. 24, 3323–3329 (1985).

    ADS  Google Scholar 

  19. 19.

    Abramson, N. Light in Flight or the Holodiagram: The Columbi Egg of Optics (SPIE, 1998).

  20. 20.

    Gkioulekas, I., Levin, A., Durand, F. & Zickler, T. Micron-scale light transport decomposition using interferometry. ACM Trans. Graph. 34, 37 (2015).

    MATH  Google Scholar 

  21. 21.

    Kadambi, A. et al. Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles. ACM Trans. Graph. 32, 1–167 (2013).

    Google Scholar 

  22. 22.

    Heide, F., Hullin, M. B., Gregson, J. & Heidrich, W. Low-budget transient imaging using photonic mixer devices. ACM Trans. Graph. 32, 45:1–45:10 (2013).

    MATH  Google Scholar 

  23. 23.

    Peters, C., Klein, J., Hullin, M. B. & Klein, R. Solving trigonometric moment problems for fast transient imaging. ACM Trans. Graph. 34, 220 (2015).

    Google Scholar 

  24. 24.

    Jarabo, A., Masia, B., Marco, J. & Gutierrez, D. Recent advances in transient imaging: a computer graphics and vision perspective. Vis. Inform. 1, 65–79 (2017).

    Google Scholar 

  25. 25.

    Velten, A. et al. Femto-photography: capturing and visualizing the propagation of light. ACM Trans. Graph. 32, 44:1–44:8 (2013).

    Google Scholar 

  26. 26.

    Gao, L., Liang, J., Li, C. & Wang, L. V. Single-shot compressed ultrafast photography at one hundred billion frames per second. Nature 516, 74–77 (2014).

    ADS  Google Scholar 

  27. 27.

    Mikami, H., Gao, L. & Goda, K. Ultrafast optical imaging technology: principles and applications of emerging methods. Nanophotonics 5, 98–110 (2016).

    Google Scholar 

  28. 28.

    Zhu, L. et al. Space- and intensity-constrained reconstruction for compressed ultrafast photography. Optica 3, 694–697 (2016).

    ADS  Google Scholar 

  29. 29.

    Laurenzis, M. & Velten, A. Nonline-of-sight laser gated viewing of scattered photons. Opt. Eng. 53, 53–53–7 (2014).

    Google Scholar 

  30. 30.

    Jarabo, A. et al. Relativistic effects for time-resolved light transport. Comput. Graph. Forum 34, 1–12 (2015).

    Google Scholar 

  31. 31.

    Laurenzis, M., Klein, J. & Bacher, E. Relativistic effects in imaging of light in flight with arbitrary paths. Opt. Lett. 41, 2001–2004 (2016).

    ADS  Google Scholar 

  32. 32.

    Clerici, M. et al. Observation of image pair creation and annihilation from superluminal scattering sources. Sci. Adv. 2, e1501691 (2016).

    ADS  Google Scholar 

  33. 33.

    Strutt, J. W. (Baron Rayleigh). Theory of Sound (MacMillan, 1896).

  34. 34.

    Becker, W. Advanced Time-Correlated Single Photon Counting Techniques (Springer, 2005).

  35. 35.

    Niclass, C., Gersbach, M., Henderson, R., Grant, L. & Charbon, E. A single photon avalanche diode implemented in 130-nm CMOS technology. IEEE J. Sel. Top. Quantum Electron. 13, 863–869 (2007).

    ADS  Google Scholar 

  36. 36.

    Richardson, J. et al. A 32×32 50 ps resolution 10 bit time to digital converter array in 130 nm CMOS for time correlated imaging. Proc. IEEE Custom Integr. Circuits Conf. 2009, 77–80 (2009).

  37. 37.

    Richardson, J. A., Webster, E. A. G., Grant, L. A. & Henderson, R. K. Scaleable single-photon avalanche diode structures in nanometer CMOS technology. IEEE Trans. Electron. Devices 58, 2028–2035 (2011).

    ADS  Google Scholar 

  38. 38.

    Gersbach, M. et al. A time-resolved, low-noise single-photon image sensor fabricated in deep-submicron CMOS technology. IEEE J. Solid-State Circuits 47, 1394–1407 (2012).

    ADS  Google Scholar 

  39. 39.

    Bronzi, D. et al. 100 000 frames/s 64 × 32 single-photon detector array for 2-D imaging and 3-D ranging. IEEE J. Sel. Top. Quantum Electron. 20, 354–363 (2014).

    ADS  Google Scholar 

  40. 40.

    Kramer, B. et al. A SPAD array detector for spectrally and lifetime resolved microscopy (Poster). 17th Int. Workshop Single Mol. Spectrosc. Ultrasensitive Anal. Life Sci. 69 (2011).

  41. 41.

    Cammi, C., Gulinatti, A., Rech, I., Panzeri, F. & Ghioni, M. Spad array module for multi-dimensional photon timing applications. J. Mod. Opt. 59, 131–139 (2012).

    ADS  Google Scholar 

  42. 42.

    Zappa, F. & Tosi, A. MiSPIA: microelectronic single-photon 3D imaging arrays for low-light high-speed safety and security applications. Proc. SPIE 8727, 87270L (2013).

  43. 43.

    Veerappan, C. et al. A 160 × 28 single-photon image sensor with on-pixel 55 ps 10 bit time-to-digital converter. Proc. IEEE Int. Solid-State Circuits Conf. 2011, 312–314 (2011).

  44. 44.

    Villa, F., Lussana, R., Tamborini, D., Tosi, A. & Zappa, F. High-fill-factor 60 × 1 SPAD array with 60 subnanosecond integrated TDCs. IEEE Photonics Technol. Lett. 27, 1261–1264 (2015).

    ADS  Google Scholar 

  45. 45.

    Burri, S., Homulle, H., Bruschini, C. & Charbon, E. LinoSPAD: a time-resolved 256 × 1 CMOS SPAD line sensor system featuring 64 FPGA-based TDC channels running at up to 8.5 giga-events per second. Proc. SPIE 9899, 98990D (2016).

  46. 46.

    Abbas, T. A. et al. Backside illuminated SPAD image sensor with 7.83 μm pitch in 3D-stacked CMOS technology. Proc. IEEE Int. Electron Devices Meet. 2016, 8.1.1–8.1.4 (2016).

  47. 47.

    Itzler, M., Jiang, X., Ben-Michael, R., Nyman, B. & Slomkowski, K. Geiger-mode APD single photon detectors. Proc. Opt. Fiber Commun. Conf. 2008, 1–3 (2008).

  48. 48.

    Itzler, M., Jiang, X., Ben-Michael, R., Nyman, B. & Slomkowski, K. Single photon avalanche photodiodes for near-infrared photon counting. SPIE Proc. 6900, 69001E (2008).

    ADS  Google Scholar 

  49. 49.

    Itzler, M. A. et al. Single photon avalanche diodes (SPADs) for 1.5 μm photon counting applications. J. Mod. Opt. 54, 283–304 (2007).

    ADS  Google Scholar 

  50. 50.

    Gariepy, G. et al. Single-photon sensitive light-in-flight imaging. Nat. Commun. 6, 6021 (2015).

  51. 51.

    Musarra, G. et al. Non-line-of-sight 3D imaging with a single-pixel camera. Phys. Rev. Appl. 12, 011002 (2019).

    ADS  Google Scholar 

  52. 52.

    Chan, S., Warburton, R., Gariepy, G., Leach, J. & Faccio, D. Non-line-of-sight tracking of people at long range. Opt. Express 25, 10109 (2017).

    Google Scholar 

  53. 53.

    Lindell, D. B., O’Toole, M. & Wetzstein, G. Towards transient imaging at interactive rates with single-photon detectors. Proc. IEEE Int. Conf. Comput. Photogr. 2018, 1–8 (2018).

  54. 54.

    Pawlikowska, A. M., Halimi, A., Lamb, R. A. & Buller, G. S. Single-photon three-dimensional imaging at up to 10 kilometers range. Opt. Express 25, 11919 (2017).

    ADS  Google Scholar 

  55. 55.

    Gariepy, G., Tonolini, F., Henderson, R., Leach, J. & Faccio, D. Detection and tracking of moving objects hidden from view. Nat. Photonics 10, 23–26 (2016). Real-time tracking of a moving NLOS object.

  56. 56.

    Buttafava, M., Zeman, J., Tosi, A., Eliceiri, K. & Velten, A. Non-line-of-sight imaging using a time-gated single photon avalanche diode. Opt. Express 23, 20997–21011 (2015).

    ADS  Google Scholar 

  57. 57.

    O’Toole, M. et al. Reconstructing transient images from single-photon sensors. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2017, 2289–2297 (2017).

  58. 58.

    Tsai, C.-Y., Kutulakos, K. N., Narasimhan, S. G. & Sankaranarayanan, A. C. The geometry of first-returning photons for non-line-of-sight imaging. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2017, 7216–7224 (2017).

  59. 59.

    Pediredla, A. K., Buttafava, M., Tosi, A., Cossairt, O. & Veeraraghavan, A. Reconstructing rooms using photon echoes: a plane based model and reconstruction algorithm for looking around the corner. Proc. IEEE Int. Conf. Comput. Photogr. 2017, 1–12 (2017).

  60. 60.

    Starshynov, I., Ghafur, O., Fitches, J. & Faccio, D. Coherent control of light for non-line-of-sight imaging. Preprint at arXiv https://arxiv.org/abs/1908.04094 (2019).

  61. 61.

    Pediredla, A., Dave, A. & Veeraraghavan, A. Snlos: Non-line-of-sight scanning through temporal focusing. Proc. EEE Int. Conf. Comput. Photogr. 2019, 1–13 (2019).

  62. 62.

    Tasinkevych, J. & Trots, I. Circular radon transform inversion technique in synthetic aperture ultrasound imaging: an ultrasound phantom evaluation. Arch. Acoust. 39, 569–582 (2014).

  63. 63.

    Moon, S. On the determination of a function from an elliptical radon transform. J. Math. Anal. Appl. 416, 724–734 (2014).

    MathSciNet  MATH  Google Scholar 

  64. 64.

    Gupta, O., Willwacher, T., Velten, A., Veeraraghavan, A. & Raskar, R. Reconstruction of hidden 3D shapes using diffuse reflections. Opt. Express 20, 19096–19108 (2012).

    ADS  Google Scholar 

  65. 65.

    Buttafava, M., Boso, G., Ruggeri, A., Mora, A. D. & Tosi, A. Time-gated single-photon detection module with 110 ps transition time and up to 80 MHz repetition rate. Rev. Sci. Instrum. 85, 083114 (2014).

    ADS  Google Scholar 

  66. 66.

    Laurenzis, M. & Velten, A. Feature selection and back-projection algorithms for nonline-of-sight laser-gated viewing. J. Electron. Imaging 23, 063003 (2014).

    ADS  Google Scholar 

  67. 67.

    Arellano, V., Gutierrez, D. & Jarabo, A. Fast back-projection for non-line of sight reconstruction. Opt. Express 25, 11574–11583 (2017).

    ADS  Google Scholar 

  68. 68.

    Kak, A. C., Slaney, M. & Wang, G. Principles of computerized tomographic imaging. Med. Phys. 29, 107–107 (2002).

    MATH  Google Scholar 

  69. 69.

    Wu, D. et al. Frequency analysis of transient light transport with applications in bare sensor imaging. Proc. 12th Eur. Conf. Comput. Vis. 7572, 542–555 (2012).

  70. 70.

    Heide, F., Xiao, L., Heidrich, W. & Hullin, M. B. Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2014, 3222–3229 (2014). A low-cost approach to NLOS imaging over short distances.

  71. 71.

    O’Toole, M., Lindell, D. B. & Wetzstein, G. Real-time non-line-of-sight imaging. In ACM SIGGRAPH Emerging Technologies 14 (ACM, 2018).

  72. 72.

    Heide, F. et al. Non-line-of-sight imaging with partial occluders and surface normals. ACM Trans. Graph. 38, 22 (2019).

    Google Scholar 

  73. 73.

    Thrampoulidis, C. et al. Exploiting occlusion in non-line-of-sight active imaging. IEEE Trans. Computational Imaging 4, 419–431 (2018).

    Google Scholar 

  74. 74.

    Xu, F. et al. Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging. Opt. Express 26, 9945–9962 (2018).

    ADS  Google Scholar 

  75. 75.

    Seidel, S. W. et al. Corner occluder computational periscopy: estimating a hidden scene from a single photograph. Proc. IEEE Int. Conf. Comput. Photogr. 2019, 1–9 (2019).

  76. 76.

    Baradad, M. et al. Inferring light fields from shadows. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2018, 6267–6275 (2018).

  77. 77.

    Xin, S. et al. A theory of Fermat paths for non-line-of-sight shape reconstruction. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2019, 6800–6809 (IEEE, 2019).

  78. 78.

    Iseringhausen, J. & Hullin, M. B. Non-line-of-sight reconstruction using efficient transient rendering. Preprint at arXiv https://arxiv.org/abs/1809.08044 (2018).

  79. 79.

    Tsai, C.-Y., Sankaranarayanan, A. C. & Gkioulekas, I. Beyond volumetric albedo — a surface optimization framework for non-line-of-sight imaging. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2019, 1545–1555 (2019).

  80. 80.

    Young, S., Lindell, D. & Wetzstein, G. Non-line-of-sight surface reconstruction using the directional light-cone transform. In IEEE Conf. Comput. Vis. Pattern Recognit. (IEEE, 2020).

  81. 81.

    Reza, S. A., La Manna, M. & Velten, A. A physical light transport model for non-line-of-sight imaging applications. Preprint at arXiv https://arxiv.org/abs/1802.01823 (2018).

  82. 82.

    Teichman, J. A. Phasor field waves: a mathematical treatment. Opt. Express 27, 27500–27506 (2019).

    ADS  Google Scholar 

  83. 83.

    Reza, S. A., Manna, M. L., Bauer, S. & Velten, A. Phasor field waves: experimental demonstrations of wave-like properties. Opt. Express 27, 32587–32608 (2019).

    ADS  Google Scholar 

  84. 84.

    Dove, J. & Shapiro, J. H. Paraxial theory of phasor-field imaging. Opt. Express 27, 18016–18037 (2019).

    ADS  Google Scholar 

  85. 85.

    Stolt, R. H. Migration by Fourier transform. Geophysics 43, 23–48 (1978).

    ADS  Google Scholar 

  86. 86.

    Margrave, G. F. & Lamoureux, M. P. Numerical Methods of Exploration Seismology: with Algorithms in MATLAB (Cambridge Univ. Press, 2018).

  87. 87.

    Callow, H. J. Signal Processing for Synthetic Aperture Sonar Image Enhancement. Thesis, Univ. Canterbury (2003).

  88. 88.

    Sheriff, R. W. Synthetic aperture beamforming with automatic phase compensation for high frequency sonars. Proc. IEEE Symp. Auton. Underwater Veh. Technol. 1992, 236–245 (1992).

  89. 89.

    Garcia, D. et al. Stolt’s fk migration for plane wave ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 60, 1853–1867 (2013).

    Google Scholar 

  90. 90.

    Cafforio, C., Prati, C. & Rocca, F. SAR data focusing using seismic migration techniques. IEEE Trans. Aerosp. Electron. Syst. 27, 194–207 (1991).

    ADS  Google Scholar 

  91. 91.

    Tancik, M., Swedish, T., Satat, G. & Raskar, R. Data-driven non-line-of-sight imaging with a traditional camera. In Imaging Appl. Opt. (Optical Society of America, 2018).

  92. 92.

    Chen, W., Daneau, S., Mannan, F. & Heide, F. Steady-state non-line-of-sight imaging. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2019, 6790–6799 (2019).

  93. 93.

    Caramazza, P. et al. Neural network identification of people hidden from view with a single-pixel, single-photon detector. Sci. Rep. 8, 11945 (2018).

    ADS  Google Scholar 

  94. 94.

    Pandharkar, R. et al. Estimating motion and size of moving non-line-of-sight objects in cluttered environments. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2011, 265–272 (2011).

  95. 95.

    Chan, S., Warburton, R., Gariepy, G., Leach, J. & Faccio, D. Real-time tracking of hidden objects with single-pixel detectors. Electron. Lett. 53, 1005–1008 (2017).

    ADS  Google Scholar 

  96. 96.

    Metzler, C. A., Lindell, D. B. & Wetzstein, G. Keyhole imaging: Non-line-of-sight imaging and tracking of moving objects along a single optical path at long standoff distances. Preprint at arXiv https://arxiv.org/abs/1912.06727 (2019).

  97. 97.

    Born, M. & Wolf, E. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light (Elsevier, 2013).

  98. 98.

    Kadambi, A., Zhao, H., Shi, B. & Raskar, R. Occluded imaging with time-of-flight sensors. ACM Trans. Graph. 35, 15 (2016).

    Google Scholar 

  99. 99.

    Pediredla, A. K., Matsuda, N., Cossairt, O. & Veeraraghavan, A. Linear systems approach to identifying performance bounds in indirect imaging. Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 2017, 6235–6239 (2017).

  100. 100.

    Liu, X., Bauer, S. & Velten, A. Analysis of feature visibility in non-line-of-sight measurements. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2019, 10140–10148 (2019).

  101. 101.

    Klein, J., Peters, C., Martín, J., Laurenzis, M. & Hullin, M. B. Tracking objects outside the line of sight using 2D intensity images. Sci. Rep. 6, 32491 (2016).

    ADS  Google Scholar 

  102. 102.

    Torralba, A. & Freeman, W. T. Accidental pinhole and pinspeck cameras: revealing the scene outside the picture. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2012, 374–381 (2012).

  103. 103.

    Batarseh, M. et al. Passive sensing around the corner using spatial coherence. Nat. Commun. 9, 3629 (2018).

    ADS  Google Scholar 

  104. 104.

    Metzler, C. A. et al. Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging. Optica 7, 63–71 (2020).

    ADS  Google Scholar 

  105. 105.

    Willomitzer, F., Li, F., Balaji, M. M., Rangarajan, P. & Cossairt, O. High resolution non-line-of-sight imaging with superheterodyne remote digital holography. In Imaging Appl. Opt. CM2A.2 (Optical Society of America, 2019).

  106. 106.

    Rangarajan, P., Willomitzer, F., Cossairt, O. & Christensen, M. P. Spatially resolved indirect imaging of objects beyond the line of sight. Proc. SPIE 11135, 124–131 (2019).

  107. 107.

    Brooks, J. & Faccio, D. A single-shot non-line-of-sight range-finder. Sensors 19, 4820 (2019).

    Google Scholar 

  108. 108.

    Nkwari, P. K. M., Sinha, S. & Ferreira, H. C. Through-the-wall radar imaging: a review. IETE Tech. Rev. 35, 631–639 (2018).

    Google Scholar 

  109. 109.

    Amin, M. G. Through-the-wall RADAR Imaging (CRC, 2011).

  110. 110.

    Sume, A. et al. Radar detection of moving targets behind corners. IEEE Trans. Geosci. Remote. Sens. 49, 2259–2267 (2011).

    ADS  Google Scholar 

  111. 111.

    Nag, S., Barnes, M. A., Payment, T. & Holladay, G. Ultrawideband through-wall radar for detecting the motion of people in real time. Proc. SPIE 4744, 48–57 (2002).

  112. 112.

    Ralston, T., Charvat, G. & Peabody, J. Real-time through-wall imaging using an ultrawideband multiple-input multiple-output (MIMO) phased array radar system. IEEE Int. Symp. Phased Array Syst. Technol. 2010, 551–558 (2010).

  113. 113.

    Zhao, M. et al. Through-wall human pose estimation using radio signals. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2018, 7356–7365 (2018).

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Acknowledgements

G.W. and A.V. acknowledge financial support from DARPA REVEAL (HR0011-16-C-0025). G.W. is supported by NSF CAREER Award (IIS 1553333), PECASE (ARO, W911NF19-1-0120) and the Visual Computing Center CCF grant (KAUST Office of Sponsored Research). D.F. is supported by the Royal Academy of Engineering under the Chairs in Emerging Technologies scheme and by the EPSRC (UK, grant no. EP/T00097X/1).

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Faccio, D., Velten, A. & Wetzstein, G. Non-line-of-sight imaging. Nat Rev Phys 2, 318–327 (2020). https://doi.org/10.1038/s42254-020-0174-8

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