[1] D. Faccio, A. Velten, G. Wetzstein. Non-line-of-sight imaging. Nat. Rev. Phys., 2020, 2: 318 .
[2] Kirmani A. Hutchison T. Davis J. Raskar R. , “Looking around the corner using transient imaging ,” in IEEE 12th International Conference on Computer Vision (ICCV) (2009 ), p. 159 .
[3] Maeda T. Satat G. Swedish T. Sinha L. Raskar R. , “Recent advances in imaging around corners ,” arXiv:1910.05613 (2019 ).
[4] M. La Manna, F. Kine, E. Breitbach, J. Jackson, T. Sultan, A. Velten. Error backprojection algorithms for non-line-of-sight imaging. IEEE Trans. Pattern Anal. Mach. Intell., 2019, 41: 1615 .
[5] M. O’Toole, D. B. Lindell, G. Wetzstein. Confocal non-line-of-sight imaging based on the light-cone transform. Nature, 2018, 555: 338 .
[6] T. Sasaki, C. Hashemi, J. R. Leger. Passive 3D location estimation of non-line-of-sight objects from a scattered thermal infrared light field. Opt. Express, 2021, 29: 43642 .
[7] J. Boger-Lombard, O. Katz. Passive optical time-of-flight for non-line-of-sight localization. Nat. Commun., 2019, 10: 3343 .
[8] C. Pei, A. Zhang, Y. Deng, F. Xu, J. Wu, D. U.-L. Li, H. Qiao, L. Fang, Q. Dai. Dynamic non-line-of-sight imaging system based on the optimization of point spread functions. Opt. Express, 2021, 29: 32349 .
[9] A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, R. Raskar. Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging. Nat. Commun., 2012, 3: 745 .
[10] X. Liu, I. Guillén, M. La Manna, J. H. Nam, S. A. Reza, T. H. Le, A. Jarabo, D. Gutierrez, A. Velten. Non-line-of-sight imaging using phasor-field virtual wave optics. Nature, 2019, 572: 620 .
[11] R. Geng, Y. Hu, Z. Lu, C. Yu, H. Li, H. Zhang, Y. Chen. Passive non-line-of-sight imaging using optimal transport. IEEE Trans. Image Process., 2021, 31: 110 .
[12] Tanaka K. Mukaigawa Y. Kadambi A. , “Polarized non-line-of-sight imaging ,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020 ), p. 2136 .
[13] O. Katz, P. Heidmann, M. Fink, S. Gigan. Non-invasive singleshot imaging through scattering layers and around corners via speckle correlations. Nat. Photonics, 2014, 8: 784 .
[14] Tancik M. Swedish T. Satat G. Raskar R. , “Data-driven non-line-of-sight imaging with a traditional camera ,” in Imaging and Applied Optics (2018 ), paper IW2B.6 .
[15] J. He, S. Wu, R. Wei, Y. Zhang. Non-line-of-sight imaging and tracking of moving objects based on deep learning. Opt. Express, 2022, 30: 16758 .
[16] C. A. Metzler, F. Heide, P. Rangarajan, M. M. Balaji, A. Viswanath, A. Veeraraghavan, R. G. Baraniuk. Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging. Optica, 2020, 7: 63 .
[17] Zhou C. Wang C.-Y. Liu Z. , “Non-line-of-sight imaging off a phong surface through deep learning ,” arXiv:2005.00007 (2020 ).
[18] A. Zhang, J. Wu, J. Suo, L. Fang, H. Qiao, D. D.-U. Li, S. Zhang, J. Fan, D. Qi, Q. Dai, C. Pei. Single-shot compressed ultrafast photography based on U-net network. Opt. Express, 2020, 28: 39299 .
[19] G. Gallego, T. Delbruck, G. M. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis, D. Scaramuzza. Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 2020, 44: 154 .
[20] Amir A. Taba B. Berg D. Melano T. McKinstry J. Di Nolfo C. Nayak T. Andreopoulos A. Garreau G. Mendoza M. Kusnitz J. Debole M. Esser S. Delbruck T. Flickner M. Modha D. , “A low power, fully event-based gesture recognition system ,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017 ), p. 7388 .
[21] H. Rebecq, R. Ranftl, V. Koltun, D. Scaramuzza. High speed and high dynamic range video with an event camera. IEEE Trans. Pattern Anal. Mach. Intell., 2021, 43: 1964 .
[22] Schaefer S. Gehrig D. Scaramuzza D. , “AEGNN: asynchronous event-based graph neural networks ,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022 ), p. 12361 .
[23] C. Saunders, J. Murray-Bruce, V. K. Goyal. Computational periscopy with an ordinary digital camera. Nature, 2019, 565: 472 .
[24] X. Lagorce, G. Orchard, F. Galluppi, B. E. Shi, R. B. Benosman. HOTS: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39: 1346 .
[25] C. Yan, X. Wang, X. Zhang, X. Li. Adaptive event address map denoising for event cameras. IEEE Sens. J., 2022, 22: 3417 .
[26] C. Wang, X. Wang, C. Yan, K. Ma. Feature representation and compression methods for event-based data. IEEE Sens. J., 2023, 23: 5109 .
[27] Zhang R. Isola P. Efros A. A. Shechtman E. Wang O. , “The unreasonable effectiveness of deep features as a perceptual metric ,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018 ), p. 586 .
[28] S. Chan, R. E. Warburton, G. Gariepy, J. Leach, D. Faccio. Non-line-of-sight tracking of people at long range. Opt. Express, 2017, 25: 10109 .