International Journal of Computer Vision

, Volume 84, Issue 3, pp 257–268 | Cite as

Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

Article
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Abstract

The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis and Argyros 2008). The proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. Three different methods have been used to qualitatively assess the improvements of the camera parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).

Keywords

Bundle adjustment Structure from motion Multi-view stereo Image-based modeling Camera calibration 
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References

  1. Baumgart, B. (1974). Geometric modeling for computer vision. Ph.D. thesis, Stanford University. Google Scholar
  2. Bouguet, J. Y. (2008). Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib_doc.
  3. Courchay, J. (2007). Auto-calibration á partir d’une séquence d’images (MVA internship report). Google Scholar
  4. DXO (2008). DxO Labs. DxO Optics Pro (http://www.dxo.com).
  5. Fischler, M., & Bolles, R. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. CACM 24(6). Google Scholar
  6. Franco, J. B., & Boyer, E. (2003). Exact polyhedral visual hulls. In BMVC. Google Scholar
  7. Fua, P. (2000). Regularized bundle-adjustment to model heads from image sequences without calibration data. International Journal of Computer Vision, 38(2), 153–171. doi: 10.1023/A:1008105802790. MATHCrossRefGoogle Scholar
  8. Furukawa, Y., & Ponce, J. (2006). Carved visual hulls for image-based modeling. In ECCV (pp. 564–577). Google Scholar
  9. Furukawa, Y., & Ponce, J. (2007). Accurate, dense, and robust multi-view stereopsis. In CVPR. Google Scholar
  10. Furukawa, Y., & Ponce, J. (2008a). Accurate camera calibration from multi-view stereo and bundle adjustment. In CVPR. Google Scholar
  11. Furukawa, Y., & Ponce, J. (2008b). PBA. http://www.cs.washington.edu/homes/furukawa/research/pba.
  12. Furukawa, Y., & Ponce, J. (2008c). PMVS. http://www.cs.washington.edu/homes/furukawa/research/pmvs.
  13. Hartley, R. I., & Zisserman, A. (2004). Multiple view geometry in computer vision. Cambridge: Cambridge University Press. MATHGoogle Scholar
  14. Hernández Esteban, C., & Schmitt, F. (2004). Silhouette and stereo fusion for 3D object modeling. CVIU 96(3). Google Scholar
  15. Hernández Esteban, C., Schmitt, F., & Cipolla, R. (2007). Silhouette coherence for camera calibration under circular motion. PAMI 29. Google Scholar
  16. Kazhdan, M., Bolitho, M., & Hoppe, H. (2006). Poisson surface reconstruction. In Symp. Geom. Proc. Google Scholar
  17. Lavest, J. M., Viala, M., & Dhome, M. (1998). Do we really need an accurate calibration pattern to achieve a reliable camera calibration? In ECCV. Google Scholar
  18. Lourakis, M., & Argyros, A. (2008). SBA: A generic sparse bundle adjustment C/C++ package based on the Levenberg-Marquardt algorithm. http://www.ics.forth.gr/~lourakis/sba/.
  19. Martinec, D., & Pajdla, T. (2007). Robust rotation and translation estimation in multiview reconstruction. In CVPR (pp. 1–8). Google Scholar
  20. Nister, D. (2004). An efficient solution to the five-point relative pose problem. PAMI 26(6). Google Scholar
  21. Pollefeys, M., Gool, L. V., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., & Koch, R. (2004). Visual modeling with a hand-held camera. International Journal of Computer Vision, 59(3), 207–232. doi: 10.1023/B:VISI.0000025798.50602.3a. CrossRefGoogle Scholar
  22. Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR. Google Scholar
  23. Sinha, S., & Pollefeys, M. (2005). Multi-view reconstruction using photo-consistency and exact silhouette constraints: A maximum-flow formulation. In ICCV. Google Scholar
  24. Tran, S., & Davis, L. (2006). 3d surface reconstruction using graph cuts with surface constraints. In ECCV. Google Scholar
  25. Triggs, B., McLauchlan, P., Hartley, R., & Fitzgibbon, A. (2000). Bundle adjustment—A modern synthesis. In W. Triggs, A. Zisserman, & R. Szeliski (Eds.). Vision algorithms: theory and practice (pp. 298–375). Berlin: Springer. CrossRefGoogle Scholar
  26. Triggs, W. (1997). Auto-calibration and the absolute quadric. In CVPR. Google Scholar
  27. Tsai, R. (1987). A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras. Robotics and Automation 3(4). Google Scholar
  28. Uffenkamp, V. (1993). State of the art of high precision industrial photogrammetry. In Third International Workshop on Accelerator Alignment, Annecy, France. Google Scholar
  29. Wong, K. K., & Cipolla, R. (2004). Reconstruction of sculpture from its profiles with unknown camera positions. IEEE Transactions on Image Processing. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Computer Science & EngineeringUniversity of WashingtonSeattleUSA
  2. 2.Willow project-team at the Laboratoire d’Informatique de l’Ecole Normale SupérieureENS/INRIA/CNRS UMR 8548Paris Cedex 05France

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