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Radial Distortion Invariant Factorization for Structure from Motion

Iglesias, José Pedro and Olsson, Carl LU (2021) 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 In Proceedings of the IEEE International Conference on Computer Vision p.5886-5895
Abstract

Factorization methods are frequently used for structure from motion problems (SfM). In the presence of noise they are able to jointly estimate camera matrices and scene points in overdetermined settings, without the need for accurate initial solutions. While the early formulations were restricted to affine models, recent approaches have been show to work with pinhole cameras by minimizing object space errors. In this paper we propose a factorization approach using the so called radial camera, which is invariant to radial distortion and changes in focal length. Assuming a known principal point our approach can reconstruct the 3D scene in settings with unknown and varying radial distortion and focal length. We show on both real and... (More)

Factorization methods are frequently used for structure from motion problems (SfM). In the presence of noise they are able to jointly estimate camera matrices and scene points in overdetermined settings, without the need for accurate initial solutions. While the early formulations were restricted to affine models, recent approaches have been show to work with pinhole cameras by minimizing object space errors. In this paper we propose a factorization approach using the so called radial camera, which is invariant to radial distortion and changes in focal length. Assuming a known principal point our approach can reconstruct the 3D scene in settings with unknown and varying radial distortion and focal length. We show on both real and synthetic data that our approach outperforms state-of-the-art factorization methods under these conditions.

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Please use this url to cite or link to this publication:
author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
series title
Proceedings of the IEEE International Conference on Computer Vision
pages
10 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
conference location
Virtual, Online, Canada
conference dates
2021-10-11 - 2021-10-17
external identifiers
  • scopus:85127763490
ISSN
1550-5499
ISBN
9781665428125
DOI
10.1109/ICCV48922.2021.00585
language
English
LU publication?
yes
id
71a868ec-6aec-48fc-b9da-c41f341bb5d5
date added to LUP
2022-06-14 13:28:26
date last changed
2022-06-14 13:28:26
@inproceedings{71a868ec-6aec-48fc-b9da-c41f341bb5d5,
  abstract     = {{<p>Factorization methods are frequently used for structure from motion problems (SfM). In the presence of noise they are able to jointly estimate camera matrices and scene points in overdetermined settings, without the need for accurate initial solutions. While the early formulations were restricted to affine models, recent approaches have been show to work with pinhole cameras by minimizing object space errors. In this paper we propose a factorization approach using the so called radial camera, which is invariant to radial distortion and changes in focal length. Assuming a known principal point our approach can reconstruct the 3D scene in settings with unknown and varying radial distortion and focal length. We show on both real and synthetic data that our approach outperforms state-of-the-art factorization methods under these conditions.</p>}},
  author       = {{Iglesias, José Pedro and Olsson, Carl}},
  booktitle    = {{Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021}},
  isbn         = {{9781665428125}},
  issn         = {{1550-5499}},
  language     = {{eng}},
  pages        = {{5886--5895}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings of the IEEE International Conference on Computer Vision}},
  title        = {{Radial Distortion Invariant Factorization for Structure from Motion}},
  url          = {{http://dx.doi.org/10.1109/ICCV48922.2021.00585}},
  doi          = {{10.1109/ICCV48922.2021.00585}},
  year         = {{2021}},
}