Rolling Shutter Camera Absolute Pose
(2020) In IEEE Transactions on Pattern Analysis and Machine Intelligence 42(6). p.1439-1452- Abstract
We present minimal, non-iterative solutions to the absolute pose problem for images from rolling shutter cameras. The absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We discuss several camera motion models and propose two feasible rolling shutter camera models for a polynomial solver. In previous work a linearized camera model was used that required an initial estimate of the camera orientation. We show how to simplify the system of equations and make this solver faster. Furthermore, we present a first solution of the non-linearized camera orientation model using the Cayley parameterization. The new solver does not require any initial camera... (More)
We present minimal, non-iterative solutions to the absolute pose problem for images from rolling shutter cameras. The absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We discuss several camera motion models and propose two feasible rolling shutter camera models for a polynomial solver. In previous work a linearized camera model was used that required an initial estimate of the camera orientation. We show how to simplify the system of equations and make this solver faster. Furthermore, we present a first solution of the non-linearized camera orientation model using the Cayley parameterization. The new solver does not require any initial camera orientation estimate and therefore serves as a standalone solution to the rolling shutter camera pose problem from six 2D-to-3D correspondences. We show that our algorithms outperform P3P followed by a non-linear refinement using a rolling shutter model.
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- author
- Albl, Cenek ; Kukelova, Zuzana ; Larsson, Viktor LU and Pajdla, Tomas
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- camera absolute pose, Computer vision, minimal problems, rolling shutter
- in
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- volume
- 42
- issue
- 6
- article number
- 8621045
- pages
- 14 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85084721776
- pmid:30676945
- ISSN
- 0162-8828
- DOI
- 10.1109/TPAMI.2019.2894395
- language
- English
- LU publication?
- yes
- id
- 2b4a8d7b-6c29-4aec-9eeb-94241c7d470e
- date added to LUP
- 2021-01-04 14:41:30
- date last changed
- 2024-05-01 23:25:25
@article{2b4a8d7b-6c29-4aec-9eeb-94241c7d470e,
abstract = {{<p>We present minimal, non-iterative solutions to the absolute pose problem for images from rolling shutter cameras. The absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We discuss several camera motion models and propose two feasible rolling shutter camera models for a polynomial solver. In previous work a linearized camera model was used that required an initial estimate of the camera orientation. We show how to simplify the system of equations and make this solver faster. Furthermore, we present a first solution of the non-linearized camera orientation model using the Cayley parameterization. The new solver does not require any initial camera orientation estimate and therefore serves as a standalone solution to the rolling shutter camera pose problem from six 2D-to-3D correspondences. We show that our algorithms outperform P3P followed by a non-linear refinement using a rolling shutter model.</p>}},
author = {{Albl, Cenek and Kukelova, Zuzana and Larsson, Viktor and Pajdla, Tomas}},
issn = {{0162-8828}},
keywords = {{camera absolute pose; Computer vision; minimal problems; rolling shutter}},
language = {{eng}},
number = {{6}},
pages = {{1439--1452}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
title = {{Rolling Shutter Camera Absolute Pose}},
url = {{http://dx.doi.org/10.1109/TPAMI.2019.2894395}},
doi = {{10.1109/TPAMI.2019.2894395}},
volume = {{42}},
year = {{2020}},
}