[1] Marrugo, A. G., Gao, F. & Zhang, S. State-of-the-art active optical techniques for three-dimensional surface metrology: a review [Invited]. Journal of the Optical Society of America A 37, B60-B77 (2020). doi: 10.1364/JOSAA.398644
[2] Leach, R. K. et al. Geometrical metrology for metal additive manufacturing. CIRP Annals 68, 677-700 (2019). doi: 10.1016/j.cirp.2019.05.004
[3] Rogers, C. et al. A universal 3D imaging sensor on a silicon photonics platform. Nature 590, 256-261 (2021). doi: 10.1038/s41586-021-03259-y
[4] Huang, X. L. et al. Polarization structured light 3D depth image sensor for scenes with reflective surfaces. Nature Communications 14, 6855 (2023). doi: 10.1038/s41467-023-42678-5
[5] Gorthi, S. S. & Rastogi, P. Fringe projection techniques: whither we are?. Optics and Lasers in Engineering 48, 133-140 (2010). doi: 10.1016/j.optlaseng.2009.09.001
[6] Ebrahim, M. A. B. 3D laser scanners’ techniques overview. International Journal of Science and Research 4, 323-331 (2015).
[7] Blake, R. & Wilson, H. Binocular vision. Vision Research 51, 754-770 (2011). doi: 10.1016/j.visres.2010.10.009
[8] Rotter, S. & Gigan, S. Light fields in complex media: Mesoscopic scattering meets wave control. Reviews of Modern Physics 89, 015005 (2017). doi: 10.1103/RevModPhys.89.015005
[9] Holzmond, O. & Li, X. D. In situ real time defect detection of 3D printed parts. Additive Manufacturing 17, 135-142 (2017). doi: 10.1016/j.addma.2017.08.003
[10] Ma, X. Z. et al. 3D object detection from images for autonomous driving: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 46, 3537-3556 (2024).
[11] Stellinga, D. et al. Time-of-flight 3D imaging through multimode optical fibers. Science 374, 1395-1399 (2021). doi: 10.1126/science.abl3771
[12] Salahieh, B. et al. Multi-polarization fringe projection imaging for high dynamic range objects. Optics Express 22, 10064-10071 (2014). doi: 10.1364/OE.22.010064
[13] Zhang, L. et al. Real-time high dynamic range 3D measurement using fringe projection. Optics Express 28, 24363-24378 (2020). doi: 10.1364/OE.398814
[14] Cai, Z. W. et al. Structured light field 3D imaging. Optics Express 24, 20324-20334 (2016). doi: 10.1364/OE.24.020324
[15] Gallego, G. et al. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 154-180 (2022). doi: 10.1109/TPAMI.2020.3008413
[16] Muglikar, M. , Gallego, G. & Scaramuzza, D. ESL: event-based structured light. 2021 International Conference on 3D Vision (3DV). London: IEEE, 2021, 1165-1174.
[17] Feng, S. J. et al. High dynamic range 3D measurements with fringe projection profilometry: a review. Measurement Science and Technology 29, 122001 (2018). doi: 10.1088/1361-6501/aae4fb
[18] Waddington, C. J. & Kofman, J. D. Modified sinusoidal fringe-pattern projection for variable illuminance in phase-shifting three-dimensional surface-shape metrology. Optical Engineering 53, 084109 (2014). doi: 10.1117/1.OE.53.8.084109
[19] Zhang, L. et al. High-speed high dynamic range 3D shape measurement based on deep learning. Optics and Lasers in Engineering 134, 106245 (2020). doi: 10.1016/j.optlaseng.2020.106245
[20] Feng, S. J. et al. Generalized framework for non-sinusoidal fringe analysis using deep learning. Photonics Research 9, 1084-1098 (2021). doi: 10.1364/PRJ.420944
[21] Liu, X. J. et al. Optical measurement of highly reflective surfaces from a single exposure. IEEE Transactions on Industrial Informatics 17, 1882-1891 (2020).
[22] Zhang, J. et al. Single-exposure optical measurement of highly reflective surfaces via deep sinusoidal prior for complex equipment production. IEEE Transactions on Industrial Informatics 19, 2039-2048 (2023). doi: 10.1109/TII.2022.3185660
[23] Nayar, S. K. et al. Fast separation of direct and global components of a scene using high frequency illumination. ACM SIGGRAPH 2006 Papers. Boston: ACM, 2006, 935-944.
[24] Gupta, M. & Nayar, S. K. Micro phase shifting. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence: IEEE, 2012, 813-820.
[25] Gupta, M. et al. Structured light 3D scanning in the presence of global illumination. CVPR 2011. Colorado Springs: IEEE, 2011, 713-720.
[26] O'Toole, M., Raskar, R. & Kutulakos, K. N. Primal-dual coding to probe light transport. ACM Transactions on Graphics 31, 39 (2012).
[27] O'Toole, M. et al. Homogeneous codes for energy-efficient illumination and imaging. ACM Transactions on Graphics 34, 35 (2015).
[28] O'Toole, M. , Mather, J. & Kutulakos, K. N. 3D shape and indirect appearance by structured light transport. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014, 3246-3253.
[29] Edgar, M. P., Gibson, G. M. & Padgett, M. J. Principles and prospects for single-pixel imaging. Nature Photonics 13, 13-20 (2019). doi: 10.1038/s41566-018-0300-7
[30] Gibson, G. M., Johnson, S. D. & Padgett, M. J. Single-pixel imaging 12 years on: a review. Optics Express 28, 28190-28208 (2020). doi: 10.1364/oe.403195
[31] Shrekenhamer, D., Watts, C. M. & Padilla, W. J. Terahertz single pixel imaging with an optically controlled dynamic spatial light modulator. Optics Express 21, 12507-12518 (2013). doi: 10.1364/OE.21.012507
[32] Watts, C. M. et al. Terahertz compressive imaging with metamaterial spatial light modulators. Nature Photonics 8, 605-609 (2014). doi: 10.1038/nphoton.2014.139
[33] Radwell, N. et al. Single-pixel infrared and visible microscope. Optica 1, 285-289 (2014). doi: 10.1364/OPTICA.1.000285
[34] Wang, Y. Q. et al. Mid-infrared single-pixel imaging at the single-photon level. Nature Communications 14, 1073 (2023). doi: 10.1038/s41467-023-36815-3
[35] Sun, B. et al. 3D computational imaging with single-pixel detectors. Science 340, 844-847 (2013). doi: 10.1126/science.1234454
[36] Sun, M. J. et al. Single-pixel three-dimensional imaging with time-based depth resolution. Nature Communications 7, 12010 (2016). doi: 10.1038/ncomms12010
[37] Peng, Y. & Chen, W. Learning-based correction with Gaussian constraints for ghost imaging through dynamic scattering media. Optics Letters 48, 4480-4483 (2023). doi: 10.1364/OL.499787
[38] Xiao, Y., Zhou, L. N. & Chen, W. Correspondence imaging through complex scattering media with temporal correction. Optics and Lasers in Engineering 174, 107957 (2024). doi: 10.1016/j.optlaseng.2023.107957
[39] Jiang, H. Z. et al. Parallel single-pixel imaging: a general method for direct–global separation and 3D shape reconstruction under strong global illumination. International Journal of Computer Vision 129, 1060-1086 (2021). doi: 10.1007/s11263-020-01413-z
[40] Zhang, Z. B., Ma, X. & Zhong, J. G. Single-pixel imaging by means of Fourier spectrum acquisition. Nature Communications 6, 6225 (2015). doi: 10.1038/ncomms7225
[41] Li, Y. X. et al. Projective parallel single-pixel imaging to overcome global illumination in 3D structure light scanning. Proceedings of the 17th European Conference on Computer Vision. Tel Aviv: Springer, 2022, 489-504.
[42] Li, B. W., Karpinsky, N. & Zhang, S. Novel calibration method for structured-light system with an out-of-focus projector. Applied Optics 53, 3415-3426 (2014). doi: 10.1364/AO.53.003415
[43] Wu, Z. J., Guo, W. B. & Zhang, Q. C. Two-frequency phase-shifting method vs. Gray-coded-based method in dynamic fringe projection profilometry: A comparative review. Optics and Lasers in Engineering 153, 106995 (2022). doi: 10.1016/j.optlaseng.2022.106995
[44] Wu, Z. J. et al. Time-overlapping structured-light projection: high performance on 3D shape measurement for complex dynamic scenes. Optics Express 30, 22467-22486 (2022). doi: 10.1364/OE.460088
[45] Wu, Z. J. et al. High-speed and high-efficiency three-dimensional shape measurement based on Gray-coded light. Photonics Research 8, 819-829 (2020). doi: 10.1364/prj.389076
[46] Papadimitriou, D. V. & Dennis, T. J. Epipolar line estimation and rectification for stereo image pairs. IEEE Transactions on Image Processing 5, 672-676 (1996). doi: 10.1109/83.491345
[47] Zhang, Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330-1334 (2000). doi: 10.1109/34.888718
[48] Scharstein, D. & Szeliski, R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7-42 (2002). doi: 10.1023/A:1014573219977