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Minimal Solutions for Relative Pose with a Single Affine Correspondence

Guan, Banglei and Zhao, Ji and Zhang, Li and Fang, Sun and Fraundorfer, Friedrich (2020) Minimal Solutions for Relative Pose with a Single Affine Correspondence. IEEE. CVPR 2020 VIRTUAL, 14.-19. 6.2020, online. doi: 10.1109/CVPR42600.2020.00200.

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Official URL: http://cvpr2020.thecvf.com/

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 closedform 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.

Item URL in elib:https://elib.dlr.de/138342/
Document Type:Conference or Workshop Item (Speech)
Title:Minimal Solutions for Relative Pose with a Single Affine Correspondence
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Guan, BangleiCollege of Aerospace Science and Engineering, Nat. University of Defense Technology, ChinaUNSPECIFIED
Zhao, Jizhaoji84 (at) gmail.comUNSPECIFIED
Zhang, LiInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUNSPECIFIED
Fang, SunCollege of Aerospace Science and Engineering, Nat. University of Defense Technology, ChinaUNSPECIFIED
Fraundorfer, Friedrichfriedrich.fraundorfer (at) dlr.deUNSPECIFIED
Date:2020
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/CVPR42600.2020.00200
Page Range:pp. 1929-1938
Publisher:IEEE
Status:Published
Keywords:affine transforms, cameras, image motion analysis, iterative methods, least squares approximations object detection, pose estimation
Event Title:CVPR 2020 VIRTUAL
Event Location:online
Event Type:international Conference
Event Dates:14.-19. 6.2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Knickl, Sabine
Deposited On:26 Nov 2020 12:37
Last Modified:14 Dec 2020 11:34

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