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Camera Pose Estimation Using Implicit Distortion Models

Pan, Linfei ; Pollefeys, Marc and Larsson, Viktor LU (2022) 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 p.12819-12828
Abstract
Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as... (More)
Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion. (Less)
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author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
pages
10 pages
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:85139033568
ISBN
978-1-6654-6946-3
978-1-6654-6947-0
DOI
10.1109/CVPR52688.2022.01248
language
English
LU publication?
yes
id
8cc7ed64-dde0-46e6-b329-3d6a6e0edbc8
date added to LUP
2022-09-06 13:25:42
date last changed
2024-06-13 19:02:12
@inproceedings{8cc7ed64-dde0-46e6-b329-3d6a6e0edbc8,
  abstract     = {{Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion.}},
  author       = {{Pan, Linfei and Pollefeys, Marc and Larsson, Viktor}},
  booktitle    = {{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}},
  isbn         = {{978-1-6654-6946-3}},
  language     = {{eng}},
  pages        = {{12819--12828}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Camera Pose Estimation Using Implicit Distortion Models}},
  url          = {{http://dx.doi.org/10.1109/CVPR52688.2022.01248}},
  doi          = {{10.1109/CVPR52688.2022.01248}},
  year         = {{2022}},
}