A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

Sean G.P. O'Brien; Jochen Trumpf; Viorela Ila; Robert Mahony

doi:10.1109/CDC.2018.8618954

A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

Sean G.P. O'Brien, Jochen Trumpf, Viorela Ila, Robert Mahony

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.

Original language	English
Title of host publication	2018 IEEE Conference on Decision and Control, CDC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	557-564
Number of pages	8
ISBN (Electronic)	9781538613955
DOIs	https://doi.org/10.1109/CDC.2018.8618954
Publication status	Published - 2 Jul 2018
Event	57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: 17 Dec 2018 → 19 Dec 2018

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2018-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	57th IEEE Conference on Decision and Control, CDC 2018
Country/Territory	United States
City	Miami
Period	17/12/18 → 19/12/18

Access to Document

10.1109/CDC.2018.8618954

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O'Brien, S. G. P., Trumpf, J., Ila, V., & Mahony, R. (2018). A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 557-564). Article 8618954 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8618954

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abstract = "This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.",

author = "O'Brien, {Sean G.P.} and Jochen Trumpf and Viorela Ila and Robert Mahony",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 57th IEEE Conference on Decision and Control, CDC 2018 ; Conference date: 17-12-2018 Through 19-12-2018",

year = "2018",

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doi = "10.1109/CDC.2018.8618954",

language = "English",

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O'Brien, SGP, Trumpf, J, Ila, V & Mahony, R 2018, A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8618954, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 557-564, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8618954

A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras. / O'Brien, Sean G.P.; Trumpf, Jochen; Ila, Viorela et al.
2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 557-564 8618954 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.

AB - This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.

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M3 - Conference contribution

T3 - Proceedings of the IEEE Conference on Decision and Control

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BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 57th IEEE Conference on Decision and Control, CDC 2018

Y2 - 17 December 2018 through 19 December 2018

ER -

A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

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