Advertisement

On Sampling Focal Length Values to Solve the Absolute Pose Problem

  • Torsten Sattler
  • Chris Sweeney
  • Marc Pollefeys
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)

Abstract

Estimating the absolute pose of a camera relative to a 3D representation of a scene is a fundamental step in many geometric Computer Vision applications. When the camera is calibrated, the pose can be computed very efficiently. If the calibration is unknown, the problem becomes much harder, resulting in slower solvers or solvers requiring more samples and thus significantly longer run-times for RANSAC. In this paper, we challenge the notion that using minimal solvers is always optimal and propose to compute the pose for a camera with unknown focal length by randomly sampling a focal length value and using an efficient pose solver for the now calibrated camera. Our main contribution is a novel sampling scheme that enables us to guide the sampling process towards promising focal length values and avoids considering all possible values once a good pose is found. The resulting RANSAC variant is significantly faster than current state-of-the-art pose solvers, especially for low inlier ratios, while achieving a similar or better pose accuracy.

Keywords

RANSAC n-point-pose (PnP) camera pose estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abidi, M.A., Chandra, T.: A New Efficient and Direct Dolution for Pose Estimation Using Quadrangular Targets: Algorithm and Evaluation. PAMI 17(5), 534–538 (1995)CrossRefGoogle Scholar
  2. 2.
    Bujnak, M., Kukelova, Z., Pajdla, T.: A General Solution To The P4P Problem for Camera With Unknown Focal Length. In: CVPR (2008)Google Scholar
  3. 3.
    Bujnak, M., Kukelova, Z., Pajdla, T.: Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part I. LNCS, vol. 5994, pp. 13–24. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Bujnak, M., Kukelova, Z., Pajdla, T.: New efficient solution to the absolute pose problem for camera with unknown focal length and radial distortion. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 11–24. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Bujnak, M., Kukelova, Z., Pajdla, T.: Making Minimal Solvers Fast. In: CVPR (2012)Google Scholar
  6. 6.
    Chum, O., Matas, J.: Randomized RANSAC with T(d,d) test. In: BMVC (2002)Google Scholar
  7. 7.
    Chum, O., Matas, J.: Optimal Randomized RANSAC. PAMI 30(8), 1472–1482 (2008)CrossRefGoogle Scholar
  8. 8.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Frahm, J.-M., et al.: 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
  10. 10.
    Haralick, R., Lee, C.N., Ottenberg, K., Nölle, M.: Review and analysis of solutions of the three point perspective pose estimation problem. IJCV 13(3), 331–356 (1994)CrossRefGoogle Scholar
  11. 11.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge Univ. Press (2004)Google Scholar
  12. 12.
    Irschara, A., Zach, C., Frahm, J.M., Bischof, H.: From Structure-from-Motion Point Clouds to Fast Location Recognition. In: CVPR (2009)Google Scholar
  13. 13.
    Josephson, K., Byröd, M.: Pose Estimation with Radial Distortion and Unknown Focal Length. In: CVPR (2009)Google Scholar
  14. 14.
    Kneip, L., Scaramuzza, D., Siegwart, R.: A Novel Parametrization of the Perspective-Three-Point Problem for a Direct Computation of Absolute Camera Position and Orientation. In: CVPR (2011)Google Scholar
  15. 15.
    Kukelova, Z., Bujnak, M., Pajdla, T.: Closed-form solutions to the minimal absolute pose problems with known vertical direction. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 216–229. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Kukelova, Z., Bujnak, M., Pajdla, T.: Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length. In: ICCV (2013)Google Scholar
  17. 17.
    Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: An Accurate O(n) Solution to the PnP Problem. IJCV 81(2), 155–166 (2009)CrossRefGoogle Scholar
  18. 18.
    Li, Y., Snavely, N., Huttenlocher, D.P.: Location Recognition using Prioritized Feature Matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide Pose Estimation Using 3D Point Clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Nister, D.: An Efficient Solution to the Five-Point Relative Pose Problem. PAMI 26(6), 756–770 (2004)CrossRefGoogle Scholar
  21. 21.
    Sattler, T., Leibe, B., Kobbelt, L.: Improving Image-Based Localization by Active Correspondence Search. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 752–765. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Sinha, S.N., Pollefeys, M.: Camera Network Calibration and Synchronization from Silhouettes in Archived Video. IJCV 87(3), 266–283 (2010)CrossRefGoogle Scholar
  23. 23.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: Exploring photo collections in 3D. In: SIGGRAPH (2006)Google Scholar
  24. 24.
    Triggs, B.: Camera Pose and Calibration from 4 or 5 Known 3D Points. In: ICCV (1999)Google Scholar
  25. 25.
    Wu, C.: Towards Linear-Time Incremental Structure from Motion. In: 3DV (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Torsten Sattler
    • 1
  • Chris Sweeney
    • 2
  • Marc Pollefeys
    • 1
  1. 1.Department of Computer ScienceETH ZürichZürichSwitzerland
  2. 2.University of California Santa BarbaraSanta BarbaraUSA

Personalised recommendations