TY - JOUR
T1 - Minimal Solvers for Relative Pose Estimation of Multi-Camera Systems using Affine Correspondences
AU - Guan, Banglei
AU - Zhao, Ji
AU - Barath, Daniel
AU - Fraundorfer, Friedrich
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs (a minimal number of one AC and one point correspondence) with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on three real-world scenes. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques. Source code is released at https://github.com/jizhaox/relative_pose_gcam_affine.
AB - We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs (a minimal number of one AC and one point correspondence) with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on three real-world scenes. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques. Source code is released at https://github.com/jizhaox/relative_pose_gcam_affine.
KW - Affine correspondence
KW - Minimal solver
KW - Multi-camera system
KW - Relative pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85140992760&partnerID=8YFLogxK
U2 - 10.1007/s11263-022-01690-w
DO - 10.1007/s11263-022-01690-w
M3 - Article
AN - SCOPUS:85140992760
SN - 0920-5691
VL - 131
SP - 324
EP - 345
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 1
ER -