[1] A. W. Lohmann. Scaling laws for lens systems. Appl. Opt., 28, 4996(1989). https://doi.org/10.1364/AO.28.004996

[2] S. Zhang. High-speed 3D shape measurement with structured light methods: a review. Opt. Lasers Eng., 106, 119(2018). https://doi.org/10.1016/j.optlaseng.2018.02.017

[3] R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision(2003).

[4] I. Chugunov, Y. Zhang, F. Heide. Shakes on a plane: unsupervised depth estimation from unstabilized photography, 13240(2023).

[5] B. Wilburn et al. High performance imaging using large camera arrays. ACM Trans. Graph., 24, 765(2005). https://doi.org/10.1145/1073204.1073259

[6] M. Harfouche et al. Imaging across multiple spatial scales with the multi-camera array microscope. Optica, 10, 471(2023). https://doi.org/10.1364/OPTICA.478010

[7] K. C. Zhou et al. Computational 3D topographic microscopy from terabytes of data per sample. J. Big Data, 11, 62(2024). https://doi.org/10.1186/s40537-024-00901-0

[8] L. Traxler et al. Experimental comparison of optical inline 3D measurement and inspection systems. IEEE Access, 9, 53952(2021). https://doi.org/10.1109/ACCESS.2021.3070381

[9] B. Freedman. Depth mapping using projected patterns(2010).

[10] S. R. Fanello et al. HyperDepth: learning depth from structured light without matching, 5441(2016).

[11] D. Scharstein, R. Szeliski. High-accuracy stereo depth maps using structured light, 195(2003).

[12] S.-H. Baek et al. Centimeter-wave free-space neural time-of-flight imaging. ACM Trans. Graph., 42, 1(2023). https://doi.org/10.1145/3522671

[13] C. Callenberg et al. Low-cost SPAD sensing for non-line-of-sight tracking, material classification and depth imaging. ACM Trans. Graph., 40, 1(2021). https://doi.org/10.1145/3450626.3459824

[14] A. Gupta et al. Asynchronous single-photon 3D imaging, 7909(2019).

[15] L. Chen et al. Calibration method for a multi-focus microscopic 3D imaging system. Opt. Lett., 48, 4348(2023). https://doi.org/10.1364/OL.498283

[16] Y. Hu et al. Microscopic fringe projection profilometry: a review. Opt. Lasers Eng., 135, 106192(2020). https://doi.org/10.1016/j.optlaseng.2020.106192

[17] J. L. Schönberger et al. Pixelwise view selection for unstructured multi-view stereo, 501(2016).

[18] Y. Hou et al. Multi-view stereo by temporal nonparametric fusion, 2651(2019).

[19] K. Wang, S. Shen. MVDepthNet: real-time multiview depth estimation neural network, 248(2018).

[20] K. C. Zhou et al. Mesoscopic photogrammetry with an unstabilized phone camera, 7531(2021).

[21] H. Ha et al. High-quality depth from uncalibrated small motion clip, 5413(2016).

[22] J. L. Schonberger, J.-M. Frahm. Structure-from-motion revisited, 4104(2016).

[23] Y. Xiong, S. A. Shafer. Depth from focusing and defocusing, 68(1993).

[24] M. Subbarao, G. Surya. Depth from defocus: a spatial domain approach. Int. J. Comput. Vis., 13, 271(1994). https://doi.org/10.1007/BF02028349

[25] W. Luo, A. G. Schwing, R. Urtasun. Efficient deep learning for stereo matching, 5695(2016).

[26] B.-U. Lee et al. CNN-based simultaneous dehazing and depth estimation, 9722(2020).

[27] F. Tosi et al. Learning monocular depth estimation infusing traditional stereo knowledge, 9799(2019).

[28] W. Yin et al. Enforcing geometric constraints of virtual normal for depth prediction, 5684(2019).

[29] C. Zhao et al. Monovit: self-supervised monocular depth estimation with a vision transformer, 668(2022).

[30] H. Si et al. Fully self-supervised depth estimation from defocus clue, 9140(2023).

[31] L. Yang et al. Depth anything: unleashing the power of large-scale unlabeled data, 10371(2024).

[32] I. Chugunov et al. The implicit values of a good hand shake: Handheld multi-frame neural depth refinement, 2852(2022).

[33] J. Watson et al. The temporal opportunist: Self-supervised multi-frame monocular depth, 1164(2021).

[34] O. Ronneberger et al. U-net: convolutional networks for biomedical image segmentation, 234(2015).

[35] M. Abadi et al. TensorFlow: large-scale machine learning on heterogeneous systems software(2015). https://www.tensorflow.org/

[36] R. P. J. Nieuwenhuizen et al. Measuring image resolution in optical nanoscopy. Nat. Methods, 10, 557(2013). https://doi.org/10.1038/nmeth.2448

[37] N. Banterle et al. Fourier ring correlation as a resolution criterion for super-resolution microscopy. J. Struct. Biol., 183, 363(2013). https://doi.org/10.1016/j.jsb.2013.05.004

[38] F. Preusser et al. FRC-QE: a robust and comparable 3D microscopy image quality metric for cleared organoids. Bioinformatics, 37, 3088(2021). https://doi.org/10.1093/bioinformatics/btab160

[39] M. Kahnt et al. Multi-slice ptychography enables high-resolution measurements in extended chemical reactors. Sci. Rep., 11, 1500(2021). https://doi.org/10.1038/s41598-020-80926-6

[40] A. Berberich et al. Fourier ring correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules. Front. Bioinform., 1, 752788(2021). https://doi.org/10.3389/fbinf.2021.752788

[41] S. Koho et al. Fourier ring correlation simplifies image restoration in fluorescence microscopy. Nat. Commun., 10, 3103(2019). https://doi.org/10.1038/s41467-019-11024-z

[42] L. Loetgering et al. Generation and characterization of focused helical x-ray beams. Sci. Adv., 6, eaax8836(2020). https://doi.org/10.1126/sciadv.aax8836

[43] B. Mildenhall et al. NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM, 65, 99(2021). https://doi.org/10.1145/3503250

[44] B. Kerbl et al. 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42, 1(2023). https://doi.org/10.1145/3592433