Single-pixel LIDAR with deep learning optimised sampling

Steven D. Johnson; Neal Radwell; Matthew P. Edgar; Catherine Higham; Roderick Murray-Smith; Miles J. Padgett

doi:10.1364/CLEO_AT.2020.ATu3T.5

Single-pixel LIDAR with deep learning optimised sampling

Steven D. Johnson, Neal Radwell, Matthew P. Edgar, Catherine Higham, Roderick Murray-Smith, Miles J. Padgett

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

Abstract

We present a LIDAR system that compressively samples a scene using a deep-learning optimised sampling basis and reconstruction algorithm. This approach improves scene reconstruction quality compared to an orthogonal sampling method.

Original language	English
Title of host publication	2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings
Subtitle of host publication	Applications and Technology, CLEO_AT 2020
ISBN (Electronic)	9781943580767
DOIs	https://doi.org/10.1364/CLEO_AT.2020.ATu3T.5
Publication status	Published - May 2020
Event	2020 Conference on Lasers and Electro-Optics, CLEO 2020 - San Jose, United States Duration: 10 May 2020 → 15 May 2020

Publication series

Name	Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS
Volume	2020-May
ISSN (Print)	1092-8081

Conference

Conference	2020 Conference on Lasers and Electro-Optics, CLEO 2020
Country/Territory	United States
City	San Jose
Period	10/05/20 → 15/05/20

Access to Document

10.1364/CLEO_AT.2020.ATu3T.5

Cite this

Johnson, S. D., Radwell, N., Edgar, M. P., Higham, C., Murray-Smith, R., & Padgett, M. J. (2020). Single-pixel LIDAR with deep learning optimised sampling. In 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings: Applications and Technology, CLEO_AT 2020 Article 9193595 (Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS; Vol. 2020-May). https://doi.org/10.1364/CLEO_AT.2020.ATu3T.5

@inproceedings{f03226bb647246789b7dcebcf0f0ac2b,

title = "Single-pixel LIDAR with deep learning optimised sampling",

abstract = "We present a LIDAR system that compressively samples a scene using a deep-learning optimised sampling basis and reconstruction algorithm. This approach improves scene reconstruction quality compared to an orthogonal sampling method.",

author = "Johnson, \{Steven D.\} and Neal Radwell and Edgar, \{Matthew P.\} and Catherine Higham and Roderick Murray-Smith and Padgett, \{Miles J.\}",

note = "Publisher Copyright: {\textcopyright} 2020 OSA.; 2020 Conference on Lasers and Electro-Optics, CLEO 2020 ; Conference date: 10-05-2020 Through 15-05-2020",

year = "2020",

month = may,

doi = "10.1364/CLEO\_AT.2020.ATu3T.5",

language = "English",

series = "Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS",

booktitle = "2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings",

}

Johnson, SD, Radwell, N, Edgar, MP, Higham, C, Murray-Smith, R & Padgett, MJ 2020, Single-pixel LIDAR with deep learning optimised sampling. in 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings: Applications and Technology, CLEO_AT 2020., 9193595, Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS, vol. 2020-May, 2020 Conference on Lasers and Electro-Optics, CLEO 2020, San Jose, United States, 10/05/20. https://doi.org/10.1364/CLEO_AT.2020.ATu3T.5

Single-pixel LIDAR with deep learning optimised sampling. / Johnson, Steven D.; Radwell, Neal; Edgar, Matthew P. et al.
2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings: Applications and Technology, CLEO_AT 2020. 2020. 9193595 (Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS; Vol. 2020-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Single-pixel LIDAR with deep learning optimised sampling

AU - Johnson, Steven D.

AU - Radwell, Neal

AU - Edgar, Matthew P.

AU - Higham, Catherine

AU - Murray-Smith, Roderick

AU - Padgett, Miles J.

PY - 2020/5

Y1 - 2020/5

N2 - We present a LIDAR system that compressively samples a scene using a deep-learning optimised sampling basis and reconstruction algorithm. This approach improves scene reconstruction quality compared to an orthogonal sampling method.

AB - We present a LIDAR system that compressively samples a scene using a deep-learning optimised sampling basis and reconstruction algorithm. This approach improves scene reconstruction quality compared to an orthogonal sampling method.

UR - https://www.scopus.com/pages/publications/85091665138

U2 - 10.1364/CLEO_AT.2020.ATu3T.5

DO - 10.1364/CLEO_AT.2020.ATu3T.5

M3 - Conference contribution book

AN - SCOPUS:85091665138

T3 - Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS

BT - 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings

T2 - 2020 Conference on Lasers and Electro-Optics, CLEO 2020

Y2 - 10 May 2020 through 15 May 2020

ER -

Johnson SD, Radwell N, Edgar MP, Higham C, Murray-Smith R, Padgett MJ. Single-pixel LIDAR with deep learning optimised sampling. In 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings: Applications and Technology, CLEO_AT 2020. 2020. 9193595. (Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS). doi: 10.1364/CLEO_AT.2020.ATu3T.5

Single-pixel LIDAR with deep learning optimised sampling

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this