Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 pp 18–33Cite as

Common Self-polar Triangle of Concentric Conics for Light Field Camera Calibration

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

Abstract

Accurate light field camera calibration plays an important role in various applications. Instead of a planar checkerboard, we propose to calibrate light field camera using a concentric conics pattern. In this paper, we explore the property and reconstruction of common self-polar triangle with respect to concentric circle and ellipse. A light field projection model is formulated to compute out an effective linear initial solution for both intrinsic and extrinsic parameters. In addition, a 4-parameter radial distortion model is presented considering different view points in light field. Finally, we establish a cost function based on Sampson error for non-linear optimization. Experimental results on both synthetic data and real light field have verified the effectiveness and robustness of the proposed algorithm.

Supported by NSFC under Grant 61531014.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Birklbauer, C., Bimber, O.: Panorama light-field imaging. In: Computer Graphics Forum, vol. 33, pp. 43–52. Wiley Online Library (2014)

    Google Scholar 

  2. Bok, Y., Jeon, H.-G., Kweon, I.S.: Geometric calibration of micro-lens-based light-field cameras using line features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 47–61. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_4

    Chapter  Google Scholar 

  3. Bok, Y., Jeon, H.G., Kweon, I.S.: Geometric calibration of micro-lens-based light field cameras using line features. IEEE T-PAMI 39(2), 287–300 (2017)

    Article  Google Scholar 

  4. Canny, J.: A computational approach to edge detection. In: Readings in Computer Vision, pp. 184–203. Elsevier (1987)

    Google Scholar 

  5. Dansereau, D.G., Mahon, I., Pizarro, O., Williams, S.B.: Plenoptic flow: closed-form visual odometry for light field cameras. In: IEEE IROS, pp. 4455–4462 (2011)

    Google Scholar 

  6. Dansereau, D.G., Pizarro, O., Williams, S.B.: Decoding, calibration and rectification for lenselet-based plenoptic cameras. In: IEEE CVPR, pp. 1027–1034 (2013)

    Google Scholar 

  7. Dong, F., Ieng, S.H., Savatier, X., Etienne-Cummings, R., Benosman, R.: Plenoptic cameras in real-time robotics. IJRR 32(2), 206–217 (2013)

    Google Scholar 

  8. Faugeras, O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge (1993)

    Google Scholar 

  9. Guo, X., Yu, Z., Kang, S.B., Lin, H., Yu, J.: Enhancing light fields through ray-space stitching. IEEE T-VCG 22(7), 1852–1861 (2016)

    Google Scholar 

  10. Hartley, R.: Self-calibration of stationary cameras. IJCV 22(1), 5–23 (1997)

    Article  Google Scholar 

  11. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  12. Huang, H., Zhang, H., Cheung, Y.M.: The common self-polar triangle of concentric circles and its application to camera calibration. In: IEEE CVPR, pp. 4065–4072 (2015)

    Google Scholar 

  13. Huang, H., Zhang, H., Cheung, Y.M.: Homography estimation from the common self-polar triangle of separate ellipses. In: IEEE CVPR, pp. 1737–1744 (2016)

    Google Scholar 

  14. Jeon, H.G., et al.: Accurate depth map estimation from a lenslet light field camera. In: IEEE CVPR, pp. 1547–1555 (2015)

    Google Scholar 

  15. Johannsen, O., Heinze, C., Goldluecke, B., Perwaß, C.: On the calibration of focused plenoptic cameras. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds.) Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. LNCS, vol. 8200, pp. 302–317. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44964-2_15

    Chapter  Google Scholar 

  16. Johannsen, O., Sulc, A., Goldluecke, B.: On linear structure from motion for light field cameras. In: IEEE ICCV, pp. 720–728 (2015)

    Google Scholar 

  17. Kim, J.S., Gurdjos, P., Kweon, I.S.: Geometric and algebraic constraints of projected concentric circles and their applications to camera calibration. IEEE T-PAMI 27(4), 637–642 (2005)

    Article  Google Scholar 

  18. Levoy, M., Hanrahan, P.: Light field rendering. In: ACM SIGGRAPH, pp. 31–42 (1996)

    Google Scholar 

  19. Liebowitz, D.: Camera calibration and reconstruction of geometry from images. Ph.D. thesis, University of Oxford (2001)

    Google Scholar 

  20. Lytro: Lytro redefines photography with light field cameras (2011). http://www.lytro.com

  21. Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non-linear Least Squares Problems, 2nd edn. Informatics and Mathematical Modelling, Technical University of Denmark, Kongens Lyngby (2004)

    Google Scholar 

  22. Ng, R.: Fourier slice photography. ACM TOG 24(3), 735–744 (2005)

    Article  Google Scholar 

  23. Ng, R.: Digital light field photography. Ph.D. thesis, Stanford University (2006)

    Google Scholar 

  24. Quan, L., Gros, P., Mohr, R.: Invariants of a pair of conics revisited. In: Mowforth, P. (ed.) BMVC 1991, pp. 71–77. Springer, London (1991). https://doi.org/10.1007/978-1-4471-1921-0_10

    Chapter  Google Scholar 

  25. Raytrix: 3D light field camera technology (2013). http://www.raytrix.de

  26. Ren, Z., Zhang, Q., Zhu, H., Wang, Q.: Extending the FOV from disparity and color consistencies in multiview light fields. In: Processing ICIP, pp. 1157–1161 (2017)

    Google Scholar 

  27. Si, L., Wang, Q.: Dense depth-map estimation and geometry inference from light fields via global optimization. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10113, pp. 83–98. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54187-7_6

    Chapter  Google Scholar 

  28. Springer, C.E.: Geometry and Analysis of Projective Spaces, vol. 68. Freeman, New York (1964)

    MATH  Google Scholar 

  29. Thomason, C., Thurow, B., Fahringer, T.: Calibration of a microlens array for a plenoptic camera. In: AIAA, pp. 1456–1460 (2014)

    Google Scholar 

  30. Vaish, V., Wilburn, B., Joshi, N., Levoy, M.: Using plane + parallax for calibrating dense camera arrays. In: IEEE CVPR, pp. 2–9 (2004)

    Google Scholar 

  31. Wang, T.C., Efros, A.A., Ramamoorthi, R.: Depth estimation with occlusion modeling using light-field cameras. IEEE T-PAMI 38(11), 2170–2181 (2016)

    Article  Google Scholar 

  32. Wanner, S., Goldluecke, B.: Globally consistent depth labeling of 4D light fields. In: IEEE CVPR, pp. 41–48 (2012)

    Google Scholar 

  33. Xiao, Z., Wang, Q., Zhou, G., Yu, J.: Aliasing detection and reduction scheme on angularly undersampled light fields. IEEE TIP 26(5), 2103–2115 (2017)

    MathSciNet  MATH  Google Scholar 

  34. Zhang, C., Ji, Z., Wang, Q.: Rectifying projective distortion in 4D light field. In: IEEE ICIP (2016)

    Google Scholar 

  35. Zhang, Q., Zhang, C., Ling, J., Wang, Q., Yu, J.: A generic multi-projection-center model and calibration method for light field cameras. IEEE T-PAMI (2018). https://doi.org/10.1109/TPAMI.2018.2864617

  36. Zhang, Y., Li, Z., Yang, W., Yu, P., Lin, H., Yu, J.: The light field 3D scanner. In: IEEE ICCP, pp. 1–9 (2017)

    Google Scholar 

  37. Zhang, Y., Yu, P., Yang, W., Ma, Y., Yu, J.: Ray space features for plenoptic structure-from-motion. In: IEEE ICCV, pp. 4631–4639 (2017)

    Google Scholar 

  38. Zhu, H., Wang, Q., Yu, J.: Occlusion-model guided anti-occlusion depth estimation in light field. IEEE J-STSP 11(7), 965–978 (2017)

    Google Scholar 

  39. Zhu, H., Zhang, Q., Wang, Q.: 4D light field superpixel and segmentation. In: IEEE CVPR, pp. 6709–6717 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Qi Zhang & Qing Wang

Authors
  1. Qi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Wang .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Q., Wang, Q. (2019). Common Self-polar Triangle of Concentric Conics for Light Field Camera Calibration. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20876-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20875-2

  • Online ISBN: 978-3-030-20876-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation