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Covariance Propagation and Next Best View Planning for 3D Reconstruction

  • Sebastian Haner
  • Anders Heyden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

This paper examines the potential benefits of applying next best view planning to sequential 3D reconstruction from unordered image sequences. A standard sequential structure-and-motion pipeline is extended with active selection of the order in which cameras are resectioned. To this end, approximate covariance propagation is implemented throughout the system, providing running estimates of the uncertainties of the reconstruction, while also enhancing robustness and accuracy. Experiments show that the use of expensive global bundle adjustment can be reduced throughout the process, while the additional cost of propagation is essentially linear in the problem size.

Keywords

Structure and motion covariance propagation next best view planning 

References

  1. 1.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the World from Internet Photo Collections. International Journal of Computer Vision 80, 189–210 (2007)CrossRefGoogle Scholar
  2. 2.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building rome in a day. In: ICCV, pp. 70–79 (2009)Google Scholar
  3. 3.
    Frahm, J.-M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.-H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: Building Rome on a Cloudless Day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Olsson, C., Enqvist, O.: Stable Structure from Motion for Unordered Image Collections. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 524–535. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.P.: Discrete-Continuous Optimization for Large-Scale Structure from Motion. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3001–3008. IEEE (2011)Google Scholar
  6. 6.
    Chen, S., Li, Y.F., Zhang, J., Wang, W.: Active Sensor Planning for Multiview Vision Tasks, 1st edn. Springer Publishing Company, Incorporated (2008)Google Scholar
  7. 7.
    Dunn, E., Olague, G., Lutton, E.: Parisian camera placement for vision metrology. Pattern Recognition Letters 27, 1209–1219 (2006)CrossRefGoogle Scholar
  8. 8.
    Wenhardt, S., Deutsch, B., Hornegger, J., Niemann, H., Denzler, J.: An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction. In: Proc. International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 103–106. IEEE Computer Society (2006)Google Scholar
  9. 9.
    Trummer, M., Munkelt, C., Denzler, J.: Online Next-Best-View Planning for Accuracy Optimization Using an Extended E-Criterion. In: Proc. International Conference on Pattern Recognition (ICPR 2010), pp. 1642–1645. IEEE Computer Society (2010)Google Scholar
  10. 10.
    Dunn, E., van den Berg, J., Frahm, J.M.: Developing visual sensing strategies through next best view planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 4001–4008 (2009)Google Scholar
  11. 11.
    Haner, S., Heyden, A.: Optimal View Path Planning for Visual SLAM. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 370–380. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Hartley, R., Zisserman, A.: Multiple View Geometry. Cambridge University Press (2003)Google Scholar
  13. 13.
    Morris, D.D.: Gauge Freedoms and Uncertainty Modeling for 3D Computer Vision. PhD thesis, Carnegie Mellon University (2001)Google Scholar
  14. 14.
    Snavely, N., Seitz, S.S.M., Szeliski, R.: Skeletal graphs for efficient structure from motion. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  15. 15.
    Kahl, F., Hartley, R.: Multiple View Geometry Under the L ∞  Norm. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1603–1617 (2008)CrossRefGoogle Scholar
  16. 16.
    Agarwal, S., Snavely, N., Seitz, S.M., Szeliski, R.: Bundle Adjustment in the Large. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 29–42. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sebastian Haner
    • 1
  • Anders Heyden
    • 1
  1. 1.Centre for Mathematical SciencesLund UniversitySweden

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