Compact Fundamental Matrix Computation

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概要

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• A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most practical of all, being small and simple. By numerical experiments, we confirm that our algorithm behaves as expected.

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著者

• Sugaya Yasuyuki

  Toyohashi Univ. Technol. Toyohashi‐shi Jpn

• Kanatani Kenichi

  Okayama Univ. Okayama‐shi Jpn

• Sugaya Yasuyuki

  Toyohashi University of Technology

関連論文

• Fast Projective Reconstruction : Toward Ultimate Efficiency
• Statistical Optimization for 3-D Reconstruction from a Single View(Image Recognition and Understanding)
• Optimal Two-View Planar Scene Triangulation
• Fast Projective Reconstruction: Toward Ultimate Efficiency
• Compact Fundamental Matrix Computation

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