Relative Pose From a Calibrated and an Uncalibrated Smartphone Image
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/df0322b2-b239-4e9b-aee4-d7fca3075144
- author
- Ding, Yaqing LU ; Barath, Daniel ; Yang, Jian and Kukelova, Zuzana
- organization
- publishing date
- 2022
- 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
- 2023-04-14 04:00:27
@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}}, }