Balanced exploration and exploitation model search for efficient epipolar geometry estimation

Liran Goshen, Ilan Shimshoni

Research output: Contribution to journalArticlepeer-review


The estimation of the epipolar geometry is especially difficult when the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. This work presents the Balanced Exploration and Exploitation Model Search (BEEM) algorithm that works very well especially for these difficult scenes. The algorithm handles these two problems in a unified manner. It includes the following main features: (1) Balanced use of three search techniques: global random exploration, local exploration near the current best solution and local exploitation to improve the quality of the model. (2) Exploits available prior information to accelerate the search process. (3) Uses the best found model to guide the search process, escape from degenerate models and to define an efficient stopping criterion. (4) Presents a simple and efficient method to estimate the epipolar geometry from two SIFT correspondences. (5) Uses the locality-sensitive hashing (LSH) approximate nearest neighbor algorithm for fast putative correspondences generation. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the state of the art algorithms.

Original languageEnglish
Pages (from-to)1230-1242
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
StatePublished - Jul 2008

Bibliographical note

Funding Information:
The authors acknowledge the support of Grant 01-99-08430 of the Israeli Space Agency through the Ministry of Science Culture and Sports of Israel.


  • 3D/stereo scene analysis
  • Computer vision
  • Motion
  • Vision and scene understanding

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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