Relative Pose Estimation With a Single Affine Correspondence

Banglei Guan, Ji Zhao, Zhang Li, Fang Sun, Friedrich Fraundorfer

Research output: Contribution to journalArticlepeer-review


In this article, we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points, and we demonstrate efficient solvers for these cases. It is shown that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and least-squares solutions, a closed-form solution for unknown focal length, and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations. The source code is released at

Original languageEnglish
Pages (from-to)10111-10122
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number10
Publication statusPublished - 1 Oct 2022


  • Affine correspondence (AC)
  • Cameras
  • Feature extraction
  • Mathematical model
  • monocular camera
  • Motion estimation
  • Pose estimation
  • relative pose estimation
  • Simultaneous localization and mapping
  • Transmission line matrix methods
  • visual odometry (VO).
  • visual odometry (VO)

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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