Light field imaging: models, calibrations, reconstructions, and applications

Review

Abstract

Light field imaging is an emerging technology in computational photography areas. Based on innovative designs of the imaging model and the optical path, light field cameras not only record the spatial intensity of threedimensional (3D) objects, but also capture the angular information of the physical world, which provides new ways to address various problems in computer vision, such as 3D reconstruction, saliency detection, and object recognition. In this paper, three key aspects of light field cameras, i.e., model, calibration, and reconstruction, are reviewed extensively. Furthermore, light field based applications on informatics, physics, medicine, and biology are exhibited. Finally, open issues in light field imaging and long-term application prospects in other natural sciences are discussed.

Key words

Light field imaging Plenoptic function Imaging model Calibration Reconstruction 

CLC number

TP391.4 

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References

  1. Babacan, S.D., Ansorge, R., Luessi, M., et al., 2012. Compressive light field sensing. IEEE Trans. Image Process., 21(12):4746–4757. https://doi.org/10.1109/tip.2012.2210237MathSciNetCrossRefMATHGoogle Scholar
  2. Belden, J., Truscott, T.T., Axiak, M.C., et al., 2010. Threedimensional synthetic aperture particle image velocimetry. Meas. Sci. Technol., 21(12):125403. https://doi.org/10.1088/0957-0233/21/12/125403CrossRefGoogle Scholar
  3. Bergamasco, F., Albarelli, A., Cosmo, L., et al., 2015. Adopting an unconstrained ray model in light-field cameras for 3D shape reconstruction. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.3003–3012. https://doi.org/10.1109/cvpr.2015.7298919Google Scholar
  4. Birklbauer, C., Bimber, O., 2014. Panorama light-field imaging. Comput. Graph. Forum, 33(2):43–52. https://doi.org/10.1111/cgf.12289CrossRefGoogle Scholar
  5. Birklbauer, C., Opelt, S., Bimber, O., 2013. Rendering gigaray light fields. Comput. Graph. Forum, 32(2pt4):469–478. https://doi.org/10.1111/cgf.12067CrossRefGoogle Scholar
  6. Bishop, T.E., Favaro, P., 2012. The light field camera: extended depth of field, aliasing, and superresolution. IEEE Trans. Patt. Anal. Mach. Intell., 34(5):972–986. https://doi.org/10.1109/tpami.2011.168CrossRefGoogle Scholar
  7. Bok, Y., Jeon, H.G., Kweon, I.S., 2014. Geometric calibration of micro-lens-based light-field cameras using line features. Proc. European Conf. on Computer Vision, p.47–61. https://doi.org/10.1007/978-3-319-10599-4_4Google Scholar
  8. Broxton, M., Grosenick, L., Yang, S., et al., 2013. Wave optics theory and 3-D deconvolution for the light field microscope. Opt. Expr., 21(21):25418–25439. https://doi.org/10.1364/oe.21.025418CrossRefGoogle Scholar
  9. Buehler, C., Bosse, M., McMillan, L., et al., 2001. Unstructured lumigraph rendering. Proc. 28th Annual Conf. on Computer Graphics and Interactive Techniques, p.425–432. https://doi.org/10.1145/383259.383309Google Scholar
  10. Chen, C., Lin, H., Yu, Z., et al., 2014. Light field stereo matching using bilateral statistics of surface cameras. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1518–1525. https://doi.org/10.1109/cvpr.2014.197Google Scholar
  11. Cho, D., Lee, M., Kim, S., et al., 2013. Modeling the calibration pipeline of the Lytro camera for high quality light-field image reconstruction. Proc. IEEE Int. Conf. on Computer Vision, p.3280–3287. https://doi.org/10.1109/iccv.2013.407Google Scholar
  12. Dansereau, D.G., Pizarro, O., Williams, S.B., 2013. Decoding, calibration and rectification for lenselet-based plenoptic cameras. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1027–1034. https://doi.org/10.1109/cvpr.2013.137Google Scholar
  13. Dansereau, D.G., Pizarro, O., Williams, S.B., 2015. Linear volumetric focus for light field cameras. ACM Trans. Graph., 34(2):15.1–15.20. https://doi.org/10.1145/2665074CrossRefGoogle Scholar
  14. Edussooriya, C.U.S., 2015. Low-Complexity Multidimensional Filters for Plenoptic Signal Processing. PhD Thesis, University of Victoria, Canada. http://hdl.handle.net/1828/6894Google Scholar
  15. Fahringer, T., Thurow, B.S., 2012. Tomographic reconstruction of a 3-D flow field using a plenoptic camera. Proc. 42nd AIAA Fluid Dynamics Conf. and Exhibit, p.1–13. https://doi.org/10.2514/6.2012-2826Google Scholar
  16. Georgiev, T., Lumsdaine, A., 2009. Superresolution with Plenoptic 2.0 cameras. Proc. Frontiers in Optics / Laser Science XXV / Fall OSA Optics & Photonics Technical Digest. https://doi.org/10.1364/srs.2009.stua6Google Scholar
  17. Georgiev, T., Lumsdaine, A., 2010. Focused plenoptic camera and rendering. J. Electron. Imag., 19(2):021106. https://doi.org/10.1117/1.3442712CrossRefGoogle Scholar
  18. Georgiev, T., Lumsdaine, A., 2012. The multifocus plenoptic camera. Proc. Digital Photography VIII. https://doi.org/10.1117/12.908667Google Scholar
  19. Georgiev, T., Zheng, K.C., Curless, B., et al., 2006. Spatioangular resolution tradeoffs in integral photography. Proc. 17th Eurographics Conf. on Rendering Techniques, p.263–272. https://doi.org/10.2312/EGWR/EGSR06/263-272Google Scholar
  20. Georgiev, T., Chunev, G., Lumsdaine, A., 2011. Superresolution with the focused plenoptic camera. Proc. Computational Imaging IX, p.78730X. https://doi.org/10.1117/12.872666CrossRefGoogle Scholar
  21. Ghasemi, A., Vetterli, M., 2014. Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.4588–4592. https://doi.org/10.1109/icassp.2014.6854471Google Scholar
  22. Gortler, S.J., Grzeszczuk, R., Szeliski, R., et al., 1996. The lumigraph. Proc. 23rd Annual Conf. on Computer Graphics and Interactive Techniques, p.43–54. https://doi.org/10.1145/237170.237200Google Scholar
  23. Guo, X., Yu, Z., Kang, S.B., et al., 2016. Enhancing light fields through ray-space stitching. IEEE Trans. Vis. Comput. Graph., 22(7):1852–1861. https://doi.org/10.1109/tvcg.2015.2476805CrossRefGoogle Scholar
  24. Hahne, C., Aggoun, A., Haxha, S., et al., 2014. Light field geometry of a standard plenoptic camera. Opt. Expr., 22(22):26659–26673. https://doi.org/10.1364/oe.22.026659CrossRefGoogle Scholar
  25. Hahne, C., Aggoun, A., Velisavljevic, V., 2015. The refocusing distance of a standard plenoptic photograph. Proc. 3DTV-Conf.: the True Vision-Capture, Transmission and Display of 3D Video, p.1-4. https://doi.org/10.1109/3dtv.2015.7169363CrossRefGoogle Scholar
  26. Iffa, E., Wetzstein, G., Heidrich, W., 2012. Light field optical flow for refractive surface reconstruction. Proc. Applications of Digital Image Processing XXXV, p.84992H. https://doi.org/10.1117/12.981608CrossRefGoogle Scholar
  27. Isaksen, A., McMillan, L., Gortler, S.J., 2000. Dynamically reparameterized light fields. Proc. 27th Annual Conf. on Computer Graphics and Interactive Techniques, p.297–306. https://doi.org/10.1145/344779.344929Google Scholar
  28. Jeon, H.G., Park, J., Choe, G., et al., 2015. Accurate depth map estimation from a lenslet light field camera. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1547–1555. https://doi.org/10.1109/cvpr.2015.7298762Google Scholar
  29. Johannsen, O., Heinze, C., Goldluecke, B., et al., 2013. On the calibration of focused plenoptic cameras. In: Grzegorzek, M., Theobalt, C., Koch, R., et al. (Eds.), Timeof-Flight and Depth Imaging: Sensors, Algorithms, and Applications, p.302–317. https://doi.org/10.1007/978-3-642-44964-2_15Google Scholar
  30. Johannsen, O., Sulc, A., Goldluecke, B., 2015. On linear structure from motion for light field cameras. Proc. IEEE Int. Conf. on Computer Vision, p.720–728. https://doi.org/10.1109/iccv.2015.89Google Scholar
  31. Johannsen, O., Sulc, A., Goldluecke, B., 2016. What sparse light field coding reveals about scene structure. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.3262–3270. https://doi.org/10.1109/cvpr.2016.355Google Scholar
  32. Kalantari, N.K., Wang, T.C., Ramamoorthi, R., 2016. Learning-based view synthesis for light field cameras. ACM Trans. Graph., 35(6):193.1-193.10. https://doi.org/10.1145/2980179.2980251CrossRefGoogle Scholar
  33. Kim, C., Zimmer, H., Pritch, Y., et al., 2013. Scene reconstruction from high spatio-angular resolution light fields. ACM Trans. Graph., 32(4):73.1-73.12. https://doi.org/10.1145/2461912.2461926MATHGoogle Scholar
  34. Kim, S., Ban, Y., Lee, S., 2014. Face liveness detection using a light field camera. Sensors, 14(12):22471–22499. https://doi.org/10.3390/s141222471CrossRefGoogle Scholar
  35. Landy, M., Movshon, J.A., 1991. The Plenoptic Function and the Elements of Early Vision. MIT Press, USA, p.3-20.Google Scholar
  36. Levin, A., Durand, F., 2010. Linear view synthesis using a dimensionality gap light field prior. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1831–1838. https://doi.org/10.1109/cvpr.2010.5539854Google Scholar
  37. Levin, A., Fergus, R., Durand, F., et al., 2007. Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph., 26(3):70. https://doi.org/10.1145/1239451.1239521CrossRefGoogle Scholar
  38. Levoy, M., Hanrahan, P., 1996. Light field rendering. Proc. 23rd Annual Conf. on Computer Graphics and Interactive Techniques, p.31–42. https://doi.org/10.1145/237170.237199Google Scholar
  39. Levoy, M., Ng, R., Adams, A., et al., 2006. Light field microscopy. ACM Trans. Graph., 25(3):924–934. https://doi.org/10.1145/1141911.1141976CrossRefGoogle Scholar
  40. Li, J., Lu, M., Li, Z.N., 2015. Continuous depth map reconstruction from light fields. IEEE Trans. Image Process., 24(11):3257–3265. https://doi.org/10.1109/tip.2015.2440760MathSciNetCrossRefGoogle Scholar
  41. Li, N., Ye, J., Ji, Y., et al., 2014. Saliency detection on light field. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.2806–2813. https://doi.org/10.1109/cvpr.2014.359Google Scholar
  42. Liang, C.K., Shih, Y.C., Chen, H.H., 2011. Light field analysis for modeling image formation. IEEE Trans. Image Process., 20(2):446–460. https://doi.org/10.1109/tip.2010.2063036MathSciNetCrossRefMATHGoogle Scholar
  43. Lin, H., Chen, C., Kang, S.B., et al., 2015. Depth recovery from light field using focal stack symmetry. Proc. IEEE Int. Conf. on Computer Vision, p.3451–3459. https://doi.org/10.1109/iccv.2015.394Google Scholar
  44. Liu, J., Xu, T., Yue, W., et al., 2015. Light-field moment microscopy with noise reduction. Opt. Expr., 23(22):29154–29162. https://doi.org/10.1364/OE.23.029154CrossRefGoogle Scholar
  45. Lumsdaine, A., Georgiev, T., 2008. Full Resolution Lightfield Rendering. Indiana University and Adobe Systems, Technical Report.Google Scholar
  46. Lytro Inc., 2011. Lytro Cinema Brings Revolutionary Light Field Technology to Film and TV Production. Technical Report. http://www.lytro.comGoogle Scholar
  47. Maeno, K., Nagahara, H., Shimada, A., et al., 2013. Light field distortion feature for transparent object recognition. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.2786–2793. https://doi.org/10.1109/cvpr.2013.359Google Scholar
  48. Marwah, K., Wetzstein, G., Bando, Y., et al., 2013. Compressive light field photography using overcomplete dictionaries and optimized projections. ACM Trans. Graph., 32(4):46.1-46.12. https://doi.org/10.1145/2461912.2461914CrossRefMATHGoogle Scholar
  49. Maximilian, D., 2016. Light-Field Imaging and Heterogeneous Light Fields. PhD Thesis, Heidelberg University, Germany.Google Scholar
  50. Mignard-Debise, L., Ihrke, I., 2015. Light-field microscopy with a consumer light-field camera. Proc. Int. Conf. on 3D Vision, p.335–343. https://doi.org/10.1109/3dv.2015.45Google Scholar
  51. Mihara, H., Funatomi, T., Tanaka, K., et al., 2016. 4D light field segmentation with spatial and angular consistencies. Proc. Int. Conf. on Computational Photography, p.54–61. https://doi.org/10.1109/iccphot.2016.7492872Google Scholar
  52. Ng, R., 2005. Fourier slice photography. ACM Trans. Graph., 24(3):735–744. https://doi.org/10.1145/1073204.1073256CrossRefGoogle Scholar
  53. Ng, R., 2006. Digital Light Field Photography. PhD Thesis, Stanford University, USA.Google Scholar
  54. Ng, R., Levoy, M., Brédif, M., et al., 2005. Light Field Photography with a Hand-Held Plenoptic Camera. Technical Report, CTSR 2005-02, Stanford University, USA.Google Scholar
  55. Niu, C.Y., Qi, H., Huang, X., et al., 2016. Efficient and robust method for simultaneous reconstruction of the temperature distribution and radiative properties in absorbing, emitting, and scattering media. J. Quant. Spectros. Rad. Transfer, 184:44–57. https://doi.org/10.1016/j.jqsrt.2016.06.032CrossRefGoogle Scholar
  56. Orth, A., Crozier, K.B., 2013. Light field moment imaging. Opt. Lett., 38(15):2666–2668. https://doi.org/10.1364/ol.38.002666CrossRefGoogle Scholar
  57. Perwaß, C., Wietzke, L., 2012. Single lens 3D-camera with extended depth-of-field. Proc. Human Vision and Electronic Imaging XVII. https://doi.org/10.1117/12.909882Google Scholar
  58. Perwaß, U., Perwaß, C., 2013. Digital Imaging System, Plenoptic Optical Device and Image Data Processing Method. US Patents.MATHGoogle Scholar
  59. Pérez, F., Pérez, A., Rodríguez, M., et al., 2012. Fourier slice super-resolution in plenoptic cameras. Proc. IEEE Int. Conf. on Computational Photography, p.1–11. https://doi.org/10.1109/iccphot.2012.6215210Google Scholar
  60. Raghavendra, R., Raja, K.B., Busch, C., 2015. Presentation attack detection for face recognition using light field camera. IEEE Trans. Image Process., 24(3):1060–1075. https://doi.org/10.1109/tip.2015.2395951MathSciNetCrossRefGoogle Scholar
  61. Sabater, N., Drazic, V., Seifi, M., et al., 2014. Light-Field Demultiplexing and Disparity Estimation. Technical Report, Technicolor Research and Innovation, France.Google Scholar
  62. Seifi, M., Sabater, N., Drazic, V., et al., 2014. Disparityguided demosaicking of light field images. Proc. IEEE Int. Conf. on Image Processing, p.5482–5486. https://doi.org/10.1109/icip.2014.7026109Google Scholar
  63. Shi, L., Hassanieh, H., Davis, A., et al., 2014. Light field reconstruction using sparsity in the continuous Fourier domain. ACM Trans. Graph., 34(1):12.1–12.13. https://doi.org/10.1145/2682631CrossRefGoogle Scholar
  64. Shum, H., Kang, S.B., 2000. Review of image-based rendering techniques. Proc. Visual Communications and Image Processing, p.2–13. https://doi.org/10.1117/12.386541Google Scholar
  65. Skupsch, C., Brücker, C., 2013. Multiple-plane particle image velocimetry using a light-field camera. Opt. Expr., 21(2):1726–1740. https://doi.org/10.1364/oe.21.001726CrossRefGoogle Scholar
  66. Srinivasan, P.P., Tao, M.W., Ng, R., et al., 2015. Oriented light-field windows for scene flow. Proc. IEEE Int. Conf. on Computer Vision, p.3496–3504. https://doi.org/10.1109/iccv.2015.399Google Scholar
  67. Tao, M.W., Hadap, S., Malik, J., et al., 2013. Depth from combining defocus and correspondence using light-field cameras. Proc. IEEE Int. Conf. on Computer Vision, p.673–680. https://doi.org/10.1109/iccv.2013.89Google Scholar
  68. Tao, M.W., Su, J.C., Wang, T.C., et al., 2016. Depth estimation and specular removal for glossy surfaces using point and line consistency with light-field cameras. IEEE Trans. Patt. Anal. Mach. Intell., 38(6):1155–1169. https://doi.org/10.1109/tpami.2015.2477811CrossRefGoogle Scholar
  69. Thomason, C.M., Thurow, B.S., Fahringer, T., 2014. Calibration of a microlens array for a plenoptic camera. Proc. 52nd Aerospace Sciences Meeting, p.1456–1460. https://doi.org/10.2514/6.2014-0396Google Scholar
  70. Thurow, B.S., Fahringer, T., 2013. Recent development of volumetric PIV with a plenoptic camera. Proc. 10th Int. Symp. on Particle Image Velocimetry, p.1–7.Google Scholar
  71. Tosic, I., Berkner, K., 2014. Light field scale-depth space transform for dense depth estimation. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.435–442. https://doi.org/10.1109/cvprw.2014.71Google Scholar
  72. Vaish, V., 2007. Synthetic Aperture Imaging Using Dense Camera Arrays. PhD Thesis, Stanford University, USA.Google Scholar
  73. Vaish, V., Garg, G., Talvala, E., et al., 2005. Synthetic aperture focusing using a shear-warp factorization of the viewing transform. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 3:129. https://doi.org/10.1109/cvpr.2005.537Google Scholar
  74. Vaish, V., Levoy, M., Szeliski, R., et al., 2006. Reconstructing occluded surfaces using synthetic apertures: stereo, focus and robust measures. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.2331–2338. https://doi.org/10.1109/cvpr.2006.244Google Scholar
  75. Venkataraman, K., Lelescu, D., Duparré, J., et al., 2013. Pi- Cam: an ultra-thin high performance monolithic camera array. ACM Trans. Graph., 32(6):166.1-166.13. https://doi.org/10.1145/2508363.2508390CrossRefGoogle Scholar
  76. Wang, T.C., Efros, A.A., Ramamoorthi, R., 2015. Occlusionaware depth estimation using light-field cameras. Proc. IEEE Int. Conf. on Computer Vision, p.3487–3495. https://doi.org/10.1109/iccv.2015.398Google Scholar
  77. Wang, T.C., Chandraker, M., Efros, A.A., et al., 2016a. SVBRDF-invariant shape and reflectance estimation from light-field cameras. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.5451–5459. https://doi.org/10.1109/cvpr.2016.588Google Scholar
  78. Wang, T.C., Zhu, J.Y., Hiroaki, E., et al., 2016b. A 4D light-field dataset and CNN architectures for material recognition. Proc. European Conf. on Computer Vision, p.121–138. https://doi.org/10.1007/978-3-319-46487-9_8Google Scholar
  79. Wanner, S., Goldluecke, B., 2012a. Globally consistent depth labeling of 4D light fields. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.41–48. https://doi.org/10.1109/cvpr.2012.6247656Google Scholar
  80. Wanner, S., Goldluecke, B., 2012b. Spatial and angular variational super-resolution of 4D light fields. Proc. European Conf. on Computer Vision, p.608–621. https://doi.org/10.1007/978-3-642-33715-4_44Google Scholar
  81. Wanner, S., Goldluecke, B., 2014. Variational light field analysis for disparity estimation and super-resolution. IEEE Trans. Patt. Anal. Mach. Intell., 36(3):606–619. https://doi.org/10.1109/tpami.2013.147CrossRefGoogle Scholar
  82. Wanner, S., Fehr, J., Jähne, B., 2011. Generating EPI representations of 4D light fields with a single lens focused plenoptic camera. Proc. Int. Symp. on Visual Computing, p.90–101. https://doi.org/10.1007/978-3-642-24028-7_9Google Scholar
  83. Wanner, S., Meister, S., Goldluecke, B., 2013. Datasets and benchmarks for densely sampled 4D light fields. In: Bronstein, M., Favre, J., Hormann, K. (Eds.), Vision, Modeling and Visualization, p.225–226. https://doi.org/10.2312/PE.VMV.VMV13.225-226Google Scholar
  84. Wilburn, B., 2004. High Performance Imaging Using Arrays of Inexpensive Cameras. PhD Thesis, Stanford University, USA.Google Scholar
  85. Williem, W., Park, I.K., 2016. Robust light field depth estimation for noisy scene with occlusion. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.4396–4404. https://doi.org/10.1109/cvpr.2016.476Google Scholar
  86. Xiao, Z., Wang, Q., Si, L., et al., 2014. Reconstructing scene depth and appearance behind foreground occlusion using camera array. Proc. IEEE Int. Conf. on Image Processing, p.41–45. https://doi.org/10.1109/icip.2014.7025007Google Scholar
  87. Xu, Y., Nagahara, H., Shimada, A., et al., 2015. TransCut: transparent object segmentation from a light-field image. Proc. IEEE Int. Conf. on Computer Vision, p.3442–3450. https://doi.org/10.1109/iccv.2015.393Google Scholar
  88. Yoon, Y., Jeon, H.G., Yoo, D., et al., 2015. Learning a deep convolutional network for light-field image superresolution. Proc. IEEE Int. Conf. on Computer Vision, p.24–32. https://doi.org/10.1109/iccvw.2015.17Google Scholar
  89. Yu, J., McMillan, L., 2004. General linear cameras. Proc. European Conf. on Computer Vision, p.14–27. https://doi.org/10.1007/978-3-540-24671-8_2Google Scholar
  90. Yu, Z., Yu, J., Lumsdaine, A., et al., 2012. An analysis of color demosaicing in plenoptic cameras. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.901–908. https://doi.org/10.1109/cvpr.2012.6247764Google Scholar
  91. Yu, Z., Guo, X., Lin, H., et al., 2013. Line assisted light field triangulation and stereo matching. Proc. IEEE Int. Conf. on Computer Vision, p.2792–2799. https://doi.org/10.1109/iccv.2013.347Google Scholar
  92. Yuan, Y., Liu, B., Li, S., et al., 2016. Light-field-camera imaging simulation of participatory media using Monte Carlo method. Int. J. Heat Mass Transfer, 102:518–527. https://doi.org/10.1016/j.ijheatmasstransfer.2016.06.053CrossRefGoogle Scholar
  93. Zhang, C., Ji, Z., Wang, Q., 2016. Rectifying projective distortion in 4D light field. Proc. IEEE Int. Conf. on Image Processing, p.1464–1468. https://doi.org/10.1109/icip.2016.7532601Google Scholar
  94. Zhang, Z., Liu, Y., Dai, Q., 2015. Light field from microbaseline image pair. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.3800–3809. https://doi.org/10.1109/cvpr.2015.7299004Google Scholar
  95. Zhou, C., Miau, D., Nayar, S.K., 2012. Focal Sweep Camera for Space-Time Refocusing. Technical Report CUCS-021-12, Department of Computure Science, Columbia University, USA.Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina

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