An Efficient Solution to 3D Reconstruction from Two Uncalibrated Views under SV Constraint

Shuyang Dou, Hiroshi Nagahashi, Xiaolin Zhang

2014

Abstract

In this paper, an efficient solution is proposed to the problem of 3D reconstruction from two uncalibrated views under Standard Vergence (SV) constraint. This solution consists of three core steps: firstly, set up the camera configuration according to SV constraint; secondly, estimate camera's focal length and relative pose between two views; lastly, reconstruct the scene optimally by minimizing reprojection error. By analysing the degenerated camera motion under SV constraint, a novel method for efficiently estimating camera's focal length and relative pose is proposed. Both synthetic and real data experiments showed that this new method could provide close estimation, which resulted in fast convergence in the most time-consuming step of final optimization. The main contribution of this paper is that it is the first time to introduce SV constraint into 3D reconstruction problem, and an efficient solution which utilizes this constraint is proposed.

References

  1. Alexiadis, D. S., Zarpalas, D. and Daras, P. (2013) Realtime, full 3-D reconstruction of moving foreground objects from multiple consumer depth cameras. IEEE Trans. Multimedia. 15(2):339-358.
  2. Bougnoux, S. (1998) From projective to Euclidean space under any practical situation, a criticism of selfcalibration. In: Proc. 6th Int. Conf. Comput. Vision, Bombay, India, 790-796.
  3. Brooks, M. J., de Agaptio, L., Huynh, D. Q. and Baumela, L. (1998) Towards robust metric reconstruction via a dynamic uncalibrated stereo head. Image Vision Comput. 16(14):989-1002.
  4. Goesele, M., Curless, B. and Seitz, S. M. (2006) Multiview stereo revisited. In: Proc. 2006 IEEE Comput. Society Conf. Comput. Vision Pattern Recog. New York, NY, USA. 2:2403-2409.
  5. Hartley, R. I. (1992) Estimation of relative camera positions for uncalibrated cameras. In: Proc. 2nd Euro. Conf. Comput. Vision, Santa Margherita Ligure, Italy, 579-587.
  6. Hartley, R. and Zisserman, A. (2004) Multiple view geometry in computer vision. 2nd ed. Cambridge: Cambridge University Press.
  7. Hartley, R. and Li H. (2012) An efficient hidden variable approach to minimal-cal camera motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 34(12):2303- 2314.
  8. Kanatani, K. and Matsunaga, C. (2000) Closed-form expression for focal lengths from the fundamental matrix. In: Proc. 4th Asian Conf. Comput. Vision, Taipei, Taiwan, 1:128-133.
  9. Kanatani, K., Nakatsuji, A. and Sugaya, Y. (2006) Stabilizing the focal length computation for 3-D reconstruction from two uncalibrated views. Int. J. Comput. Vision. 66(2):109-122.
  10. Kitware Inc., Sandia National Laboratories and Computational Simulation Software, LLC. (2013) ParaView (Version 4.0.1) [Computer program]. Available from http://www.paraview.org [Accessed 22 Jul. 2013].
  11. Kukelova, Z., Bujnak, M. and Pajdla, T. (2008) Polynomial eigenvalue solutions to the 5-pt and 6-pt relative pose problems. In: Proc. British Machine Vision Conf. Leeds, UK, 565-574.
  12. Pan, H.-P., Brooks, M. J. and Newsam, G. N. (1995a) Image resituation: initial theory. In: Proc. SPIE: Videometrics IV, Philadelphia, PA, USA, 2598:162- 173.
  13. Pan, H.-P., Huynh, D. Q. and Hamlyn, G. K. (1995b) Two-image resituation: practical algorithm. In: Proc. SPIE: Videometrics IV, Philadelphia, PA, USA, 2598:174-190.
  14. Pernek, A. and Hajder, L. (2013) Automatic focal length estimation as an eigenvalue problem. Pattern Recogn. Lett. 24(9):1108-1117.
  15. Stewenius, H., Nister, D., Kahl, F. and Schaffalitzky, F. (2005) A minimal solution for relative pose with unknown focal length. In: Proc. 2005 IEEE Comput. Society Conf. Comput. Vision Pattern Recog. San Diego, CA, USA, 2:789-794.
  16. Yamada, K., Kanazawa, Y., Kanatani, K. and Sugaya, Y. (2009) 3DRec-MATLAB [Computer Program]. Available from: http://www.img.cs.tut.ac.jp/programs/index.html [Accessed 22 Jul. 2013].
  17. Zhang, Z. (2000) A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11):1330-1334.
  18. Zhen, Z., Miao, Y., Sato, M. and Zhang, X. (2010) Automatic 3D photographing device, In: Proc. ASIAGRAPH, Tokyo, Japan, 4(1):235-237.
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Paper Citation


in Harvard Style

Dou S., Nagahashi H. and Zhang X. (2014). An Efficient Solution to 3D Reconstruction from Two Uncalibrated Views under SV Constraint . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 664-671. DOI: 10.5220/0004748006640671


in Bibtex Style

@conference{visapp14,
author={Shuyang Dou and Hiroshi Nagahashi and Xiaolin Zhang},
title={An Efficient Solution to 3D Reconstruction from Two Uncalibrated Views under SV Constraint},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={664-671},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004748006640671},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - An Efficient Solution to 3D Reconstruction from Two Uncalibrated Views under SV Constraint
SN - 978-989-758-009-3
AU - Dou S.
AU - Nagahashi H.
AU - Zhang X.
PY - 2014
SP - 664
EP - 671
DO - 10.5220/0004748006640671