Minimal Solutions to Relative Pose Estimation From Two Views Sharing a Common Direction With Unknown Focal Length
(2020) 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020- Abstract
- We propose minimal solutions to relative pose estimation problem from two views sharing a common direction with unknown focal length. This is relevant for cameras equipped with an IMU (inertial measurement unit), e.g., smart phones, tablets. Similar to the 6-point algorithm for two cameras with unknown but equal focal lengths and 7-point algorithm for two cameras with different and unknown focal lengths, we derive new 4- and 5-point algorithms for these two cases, respectively. The proposed algorithms can cope with coplanar points, which is a degenerate configuration for these 6- and 7-point counterparts. We present a detailed analysis and comparisons with the state of the art. Experimental results on both synthetic data and real images... (More)
- We propose minimal solutions to relative pose estimation problem from two views sharing a common direction with unknown focal length. This is relevant for cameras equipped with an IMU (inertial measurement unit), e.g., smart phones, tablets. Similar to the 6-point algorithm for two cameras with unknown but equal focal lengths and 7-point algorithm for two cameras with different and unknown focal lengths, we derive new 4- and 5-point algorithms for these two cases, respectively. The proposed algorithms can cope with coplanar points, which is a degenerate configuration for these 6- and 7-point counterparts. We present a detailed analysis and comparisons with the state of the art. Experimental results on both synthetic data and real images from a smart phone demonstrate the usefulness of the proposed algorithms. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ebfb7d23-6596-4e43-b47b-acbf162390cc
- author
- Ding, Yaqing LU ; Yang, Jian ; Ponce, Jean and Kong, Hui
- publishing date
- 2020
- 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
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
- conference location
- Virtual, Online, United States
- conference dates
- 2020-06-14 - 2020-06-19
- external identifiers
-
- scopus:85094825049
- ISBN
- 978-1-7281-7169-2
- 978-1-7281-7168-5
- DOI
- 10.1109/CVPR42600.2020.00707
- language
- English
- LU publication?
- no
- id
- ebfb7d23-6596-4e43-b47b-acbf162390cc
- date added to LUP
- 2022-09-08 11:34:55
- date last changed
- 2024-08-06 21:08:38
@inproceedings{ebfb7d23-6596-4e43-b47b-acbf162390cc, abstract = {{We propose minimal solutions to relative pose estimation problem from two views sharing a common direction with unknown focal length. This is relevant for cameras equipped with an IMU (inertial measurement unit), e.g., smart phones, tablets. Similar to the 6-point algorithm for two cameras with unknown but equal focal lengths and 7-point algorithm for two cameras with different and unknown focal lengths, we derive new 4- and 5-point algorithms for these two cases, respectively. The proposed algorithms can cope with coplanar points, which is a degenerate configuration for these 6- and 7-point counterparts. We present a detailed analysis and comparisons with the state of the art. Experimental results on both synthetic data and real images from a smart phone demonstrate the usefulness of the proposed algorithms.}}, author = {{Ding, Yaqing and Yang, Jian and Ponce, Jean and Kong, Hui}}, booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}}, isbn = {{978-1-7281-7169-2}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Minimal Solutions to Relative Pose Estimation From Two Views Sharing a Common Direction With Unknown Focal Length}}, url = {{http://dx.doi.org/10.1109/CVPR42600.2020.00707}}, doi = {{10.1109/CVPR42600.2020.00707}}, year = {{2020}}, }