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An Iterative Correction Phase of Light Field for Novel View Reconstruction

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13142))

Abstract

We present an iterative correction phase algorithm (ICPA) for light field reconstruction. We study novel views of light field that satisfy certain conditions can be reconstructed from the phase spectrum. The ICPA includes phase corrections in both time domain and frequency domain of discrete light filed. Furthermore, the phase corrections are “light field truncation” in the time domain and “phase replacement” in the frequency domain. Thus, the estimation of the reconstructed light field improves with each iteration. Our ICPA predicts the characteristics of light field such as phase and amplitude. Predictions on the frequency content can then be used to improve the rendering quality of novel views. Finally, to verify the claimed performance, we also compare the ICPA with the most advanced light field reconstruction algorithms. The experimental results show that the proposed ICPA outperforms other known reconstruction schemes.

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References

  1. Gortler, S., Grzeszczuk, R., Szeliski, R., Cohen, M.: The lumigraph. In: Proceedings of SIGGRAPH, pp. 43–54 (1996)

    Google Scholar 

  2. Levoy, M., Hanrahan, P.: Light field rendering. In: Proceedings of SIGGRAPH, New Orleans, USA, pp. 31–40 (1996)

    Google Scholar 

  3. Levoy, M.: Light fields and computational imaging. IEEE Comput. 8, 46–55 (2006)

    Article  Google Scholar 

  4. Berent, J., Dragotti, P.L.: Plenoptic manifolds. IEEE Signal Process. Mag. 24(6), 34–44 (2007)

    Article  Google Scholar 

  5. Koniaris, C., Kosek, M., Sinclair, D., Mitchell, K.: Compressed animated light fields with real-time view-dependent reconstruction. IEEE Trans. Vis. Comput. Graph. 25(4), 1666–1680 (2019)

    Article  Google Scholar 

  6. Jung, H., Lee, H.J., Rhee, C.E.: Flexibly connectable light field system for free view exploration. IEEE Trans. Multimedia 22(4), 980–991 (2019)

    Article  Google Scholar 

  7. Chai, J.-X., Tong, X., Chan, S.-C., Shum, H.-Y.: Plenoptic sampling. In: Proceedings of SIGGRAPH, New York, NY, USA, pp. 307–318 (2000)

    Google Scholar 

  8. Zhang, C., Chen, T.: Spectral analysis for sampling image-based rendering data. IEEE Trans. Circuits Syst. Video Technol. 13(11), 1038–1050 (2003)

    Article  Google Scholar 

  9. Do, M.N., Marchand-Maillet, D., Vetterli, M.: On the bandwidth of the plenoptic function. IEEE Trans. Image Process. 21(2), 708–717 (2012)

    Article  MathSciNet  Google Scholar 

  10. Gilliam, C., Dragotti, P., Brookes, M.: On the spectrum of the plenoptic function. IEEE Trans. Image Process. 23(2), 502–516 (2014)

    Article  MathSciNet  Google Scholar 

  11. Zhu, C.-J., Yu, L.: Spectral analysis of image-based rendering data with scene geometry. Multimedia Syst. 23(5), 627–644 (2016). https://doi.org/10.1007/s00530-016-0515-8

    Article  MathSciNet  Google Scholar 

  12. Zhu, C., Yu, L., Yan, Z., Xiang, S.: Frequency estimation of the plenoptic function using the autocorrelation theorem. IEEE Trans. Comput. Imaging 3(4), 966–981 (2017)

    Article  MathSciNet  Google Scholar 

  13. Durand, F., Holzschuch, N., Soler, C., Chan, E., Sillion, F.X.: A frequency analysis of light transport. ACM Trans. Graph. 24(3), 1115–1126 (2005)

    Article  Google Scholar 

  14. Buehler, C., Bosse, M., McMillan, L., Gortler, S.J., Cohen, M.F.: Unstructured lumigraph rendering. In: Proceedings of SIGGRAPH, pp. 425–432 (2001)

    Google Scholar 

  15. Chaurasia, G., Sorkine-Hornung, O., Drettakis, G.: Silhouette-aware warping for image-based rendering. Proc. Comput. Graph. Forum 30(4), 1223–1232 (2011)

    Article  Google Scholar 

  16. Jin, J., Hou, J., Chen, J., Zeng, H., Kwong, S., Yu, J.: Deep coarse-to-fine dense light field reconstruction with flexible sampling and geometry-aware fusion. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https://doi.org/10.1109/TPAMI.2020.3026039

  17. Meng, N., So, H.K.-H., Sun, X., Lam, E.Y.: High-dimensional dense residual convolutional neural network for light field reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 873–886 (2021)

    Article  Google Scholar 

  18. Stewart, J., Yu, J., Gortler, S.J., McMillan, L.: A new reconstruction filter for undersampled light fields. In: ACM International Conference Proceeding Series, pp. 150–156, June 2003

    Google Scholar 

  19. Hoshino, H., Okano, F., Yuyama, I.: A study on resolution and aliasing for multi-viewpoint image acquisition. IEEE Trans. Circuits Syst. Video Technol. 10(3), 366–375 (2000)

    Article  Google Scholar 

  20. Wu, G., Zhao, M., Wang, L., Dai, Q., Chai, T., Liu, Y.: Light field reconstruction using deep convolutional network on EPI. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1638–1646 (2017)

    Google Scholar 

  21. Wu, G., Liu, Y., Fang, L., Dai, Q., Chai, T.: Light field reconstruction using convolutional network on EPI and extended applications. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1681–1694 (2018)

    Article  Google Scholar 

  22. Bolles, R., Baker, H., Marimont, D.: Epipolar-plane image analysis: an approach to determining structure from motion. Int. J. Comput. Vis. 1(1), 7–55 (1987)

    Article  Google Scholar 

  23. Vagharshakyan, S., Bregovic, R., Gotchev, A.: Light field reconstruction using Shearlet transform. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 133–147 (2018)

    Article  Google Scholar 

  24. Shi, L., Hassanieh, H., Davis, A., Katabi, D., Durand, F.: Light field reconstruction using sparsity in the continuous fourier domain. ACM Trans. Graph. (TOG) 34(1), 12 (2014)

    Article  Google Scholar 

  25. Farrugia, R., Guillemot, C.: Light field super-resolution using a low-rank prior and deep convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(5), 1162–1175 (2019)

    Google Scholar 

  26. Le Pendu, M., Guillemot, C., Smolic, A.: A fourier disparity layer representation for light fields. IEEE Trans. Image Process. 28(11), 5740–5753 (2019)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China under Grant 61961005 and 61871437, and in part by the Guangxi Natural Science Foundation Project 2019AC20121 (AD19245085) and 2018GXNSFAA281195, and in part by the Natural Science Foundation of Jiangxi Province under Grant YG2018042 and Grant 20202BAB212003.

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Zhu, C., Zhang, H., Wei, Y., He, N., Liu, Q. (2022). An Iterative Correction Phase of Light Field for Novel View Reconstruction. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98354-3

  • Online ISBN: 978-3-030-98355-0

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