Corner-based geometric calibration of multi-focus plenoptic cameras

Conference paper


Nousias, S., Chadebecq, F., Pichat, J., Keane, P., Ourselin, S. and Bergeles, C. 2017. Corner-based geometric calibration of multi-focus plenoptic cameras. 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy 22 - 29 Oct 2017 IEEE. pp. 957-965 https://doi.org/10.1109/ICCV.2017.109
TypeConference paper
TitleCorner-based geometric calibration of multi-focus plenoptic cameras
AuthorsNousias, S., Chadebecq, F., Pichat, J., Keane, P., Ourselin, S. and Bergeles, C.
Abstract

We propose a method for geometric calibration of multi-focus plenoptic cameras using raw images. Multi-focus plenoptic cameras feature several types of micro-lenses spatially aligned in front of the camera sensor to generate micro-images at different magnifications. This multi-lens arrangement provides computational-photography benefits but complicates calibration. Our methodology achieves the detection of the type of micro-lenses, the retrieval of their spatial arrangement, and the estimation of intrinsic and extrinsic camera parameters therefore fully characterising this specialised camera class. Motivated from classic pinhole camera calibration, our algorithm operates on a checker-board's corners, retrieved by a custom micro-image corner detector. This approach enables the introduction of a reprojection error that is used in a minimisation framework. Our algorithm compares favourably to the state-of-the-art, as demonstrated by controlled and freehand experiments, making it a first step towards accurate 3D reconstruction and Structure-from-Motion.

Conference2017 IEEE International Conference on Computer Vision (ICCV)
Page range957-965
Proceedings Title2017 IEEE International Conference on Computer Vision (ICCV)
ISSN
Electronic1550-5499
ISBN
Electronic9781538610329
Hardcover9781538610336
PublisherIEEE
Publication dates
Print25 Dec 2017
Publication process dates
Accepted2017
Deposited28 Feb 2024
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1109/ICCV.2017.109
Web of Science identifierWOS:000425498401002
LanguageEnglish
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