Why having 10,000 parameters in your camera model is better than twelve
(2020) 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 p.2532-2541- Abstract
- Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we... (More)
- Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d0021e13-c800-44f7-a985-fb1a607403df
- author
- Schöps, Thomas ; Larsson, Viktor LU ; Pollefeys, Marc and Sattler, Torsten
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- pages
- 10 pages
- 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:85094643022
- DOI
- 10.1109/CVPR42600.2020.00261
- language
- English
- LU publication?
- no
- id
- d0021e13-c800-44f7-a985-fb1a607403df
- date added to LUP
- 2022-09-06 11:46:31
- date last changed
- 2022-10-07 12:40:38
@inproceedings{d0021e13-c800-44f7-a985-fb1a607403df,
abstract = {{Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone.}},
author = {{Schöps, Thomas and Larsson, Viktor and Pollefeys, Marc and Sattler, Torsten}},
booktitle = {{2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
language = {{eng}},
pages = {{2532--2541}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Why having 10,000 parameters in your camera model is better than twelve}},
url = {{http://dx.doi.org/10.1109/CVPR42600.2020.00261}},
doi = {{10.1109/CVPR42600.2020.00261}},
year = {{2020}},
}