Minimal Solutions for Relative Pose with a Single Affine Correspondence

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper 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 a least-squares solution, 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 from the KITTI benchmark. 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
Original languageEnglish
Title of host publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE Publications
Pages1926-1935
Number of pages10
ISBN (Electronic)978-1-7281-7168-5
DOIs
Publication statusPublished - 15 Jun 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2020 - virtuell, Virtual, United States
Duration: 14 Jun 202019 Jun 2020

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2020
CountryUnited States
CityVirtual
Period14/06/2019/06/20

Keywords

  • cs.CV

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Fields of Expertise

  • Information, Communication & Computing

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