A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus

  • Rahul Raguram
  • Jan-Michael Frahm
  • Marc Pollefeys
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)


The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. Relatively fewer efforts, however, have been directed towards formulating RANSAC in a manner that is suitable for real-time implementation. The contributions of this work are two-fold: First, we provide a comparative analysis of the state-of-the-art RANSAC algorithms and categorize the various approaches. Second, we develop a powerful new framework for real-time robust estimation. The technique we develop is capable of efficiently adapting to the constraints presented by a fixed time budget, while at the same time providing accurate estimation over a wide range of inlier ratios. The method shows significant improvements in accuracy and speed over existing techniques.


  1. 1.
    Fisher, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Matas, J., Chum, O.: Randomized RANSAC with T d,d test. Image and Vision Computing 22(10), 837–842 (2004)CrossRefGoogle Scholar
  3. 3.
    Capel, D.: An effective bail-out test for RANSAC consensus scoring. In: Proc. BMVC, pp. 629–638 (2005)Google Scholar
  4. 4.
    Matas, J., Chum, O.: Randomized ransac with sequential probability ratio test. In: Proc. ICCV, pp. 1727–1732 (2005)Google Scholar
  5. 5.
    Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Tordoff, B., Murray, D.W.: Guided sampling and consensus for motion estimation. In: Proc. ECCV, pp. 82–98 (2002)Google Scholar
  7. 7.
    Chum, O., Matas, J.: Matching with PROSAC - progressive sample consensus. In: Proc. CVPR, vol. 1, pp. 220–226 (2005)Google Scholar
  8. 8.
    Nister, D.: Preemptive RANSAC for live structure and motion estimation. In: Proc. ICCV, vol. 1, pp. 199–206 (2003)Google Scholar
  9. 9.
    Chum, O., Werner, T., Matas, J.: Two-view geometry estimation unaffected by a dominant plane. In: Proc. CVPR, pp. I: 772–779 (2005) Google Scholar
  10. 10.
    Frahm, J.M., Pollefeys, M.: Ransac for (quasi-)degenerate data (QDEGSAC). In: Proc. CVPR, pp. 453–460 (2006)Google Scholar
  11. 11.
    Torr, P., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. CVIU, 138–156 (2000)Google Scholar
  12. 12.
    Chum, O., Matas, J.: Optimal randomized RANSAC. Pattern Analysis and Machine Intelligence (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rahul Raguram
    • 1
  • Jan-Michael Frahm
    • 1
  • Marc Pollefeys
    • 1
    • 2
  1. 1.Department of Computer ScienceThe University of North Carolina at Chapel HillUSA
  2. 2.Department of Computer ScienceETH ZürichSwitzerland

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