Accelerated Hypothesis Generation for Multi-structure Robust Fitting

  • Tat-Jun Chin
  • Jin Yu
  • David Suter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)


Random hypothesis generation underpins many geometric model fitting techniques. Unfortunately it is also computationally expensive. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points to have arisen from the same model and is obtained without recourse to domain knowledge (e.g. keypoint matching scores) typically used in previous sampling enhancement methods. More crucially our approach is naturally capable of handling data with multiple model instances and excels in applications (e.g. multi-homography fitting) which easily frustrate other techniques. Experiments show that our method provides superior efficiency on various geometric model estimation tasks. Implementation of our algorithm is available on the authors’ homepage.


Image Pair Sampling Step Minimal Subset Sequential Probability Ratio Test Inlier Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tat-Jun Chin
    • 1
  • Jin Yu
    • 1
  • David Suter
    • 1
  1. 1.School of Computer ScienceThe University of Adelaide

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