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Relative Pose From a Calibrated and an Uncalibrated Smartphone Image

Ding, Yaqing LU ; Barath, Daniel ; Yang, Jian and Kukelova, Zuzana (2022) 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Abstract
In this paper, we propose a new minimal and a non-minimal solver for estimating the relative camera pose together with the unknown focal length of the second camera. This configuration has a number of practical benefits, e.g., when processing large-scale datasets. Moreover, it is resistant to the typical degenerate cases of the traditional six-point algorithm. The minimal solver requires four point correspondences and exploits the gravity direction that the built-in IMU of recent smart devices recover. We also propose a linear solver that enables estimating the pose from a larger-than-minimal sample extremely efficiently which then can be improved by, e.g., bundle adjustment. The methods are tested on 35654 image pairs from publicly... (More)
In this paper, we propose a new minimal and a non-minimal solver for estimating the relative camera pose together with the unknown focal length of the second camera. This configuration has a number of practical benefits, e.g., when processing large-scale datasets. Moreover, it is resistant to the typical degenerate cases of the traditional six-point algorithm. The minimal solver requires four point correspondences and exploits the gravity direction that the built-in IMU of recent smart devices recover. We also propose a linear solver that enables estimating the pose from a larger-than-minimal sample extremely efficiently which then can be improved by, e.g., bundle adjustment. The methods are tested on 35654 image pairs from publicly available real-world and new datasets. When combined with a recent robust estimator, they lead to results superior to the traditional solvers in terms of rotation, translation and focal length accuracy, while being notably faster. (Less)
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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
conference location
New Orleans, United States
conference dates
2022-06-19 - 2022-06-24
external identifiers
  • scopus:85142276026
DOI
10.1109/CVPR52688.2022.01243
language
English
LU publication?
yes
id
df0322b2-b239-4e9b-aee4-d7fca3075144
alternative location
https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Relative_Pose_From_a_Calibrated_and_an_Uncalibrated_Smartphone_Image_CVPR_2022_paper.pdf
date added to LUP
2022-09-07 17:39:19
date last changed
2025-04-04 14:47:19
@inproceedings{df0322b2-b239-4e9b-aee4-d7fca3075144,
  abstract     = {{In this paper, we propose a new minimal and a non-minimal solver for estimating the relative camera pose together with the unknown focal length of the second camera. This configuration has a number of practical benefits, e.g., when processing large-scale datasets. Moreover, it is resistant to the typical degenerate cases of the traditional six-point algorithm. The minimal solver requires four point correspondences and exploits the gravity direction that the built-in IMU of recent smart devices recover. We also propose a linear solver that enables estimating the pose from a larger-than-minimal sample extremely efficiently which then can be improved by, e.g., bundle adjustment. The methods are tested on 35654 image pairs from publicly available real-world and new datasets. When combined with a recent robust estimator, they lead to results superior to the traditional solvers in terms of rotation, translation and focal length accuracy, while being notably faster.}},
  author       = {{Ding, Yaqing and Barath, Daniel and Yang, Jian and Kukelova, Zuzana}},
  booktitle    = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Relative Pose From a Calibrated and an Uncalibrated Smartphone Image}},
  url          = {{http://dx.doi.org/10.1109/CVPR52688.2022.01243}},
  doi          = {{10.1109/CVPR52688.2022.01243}},
  year         = {{2022}},
}