Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background

Abderrahim Halimi; Jakeoung Koo; Robert A. Lamb; Gerald Stuart Buller; Stephen McLaughlin

doi:10.1109/ICASSP43922.2022.9746166

Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background

Abderrahim Halimi
, Jakeoung Koo
, Robert A. Lamb
, Gerald Stuart Buller
, Stephen McLaughlin

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

3 Citations (Scopus)

85 Downloads (Pure)

Abstract

This paper presents a new Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data ac- quired in extreme conditions. We focus on imaging through obscurants (i.e., fog, water) leading to high and possibly non-uniform background noise. The proposed hierarchical Bayesian method accounts for multiscale information to pro- vide distribution estimates for the target’s depth and reflec- tivity, i.e., point and uncertainty measures of the estimates to improve decision making. The correlations between variables are enforced using a weighting scheme that allows the incor- poration of guide information available from other sensors or state-of-the-art algorithms. Results on synthetic and real data show improved reconstruction of the scene in extreme conditions when compared to the state-of-the-art algorithms.

Original language	English
Title of host publication	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher	IEEE
Pages	1531-1535
Number of pages	5
ISBN (Electronic)	9781665405409
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9746166
Publication status	Published - 27 Apr 2022
Event	IEEE International Conference on Acoustics, Speech and Signal Processing 2022 - , Singapore Duration: 22 May 2022 → 27 May 2022 https://2022.ieeeicassp.org/

Conference

Conference	IEEE International Conference on Acoustics, Speech and Signal Processing 2022
Abbreviated title	IEEE ICASSP 2022
Country/Territory	Singapore
Period	22/05/22 → 27/05/22
Internet address	https://2022.ieeeicassp.org/

Keywords

3D reconstruction
Bayesian inference
multispectral Lidar imaging
obscurants
robust estimation

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP43922.2022.9746166

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Accepted author manuscript, 3.67 MB

Cite this

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title = "Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background",

abstract = "This paper presents a new Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data ac- quired in extreme conditions. We focus on imaging through obscurants (i.e., fog, water) leading to high and possibly non-uniform background noise. The proposed hierarchical Bayesian method accounts for multiscale information to pro- vide distribution estimates for the target{\textquoteright}s depth and reflec- tivity, i.e., point and uncertainty measures of the estimates to improve decision making. The correlations between variables are enforced using a weighting scheme that allows the incor- poration of guide information available from other sensors or state-of-the-art algorithms. Results on synthetic and real data show improved reconstruction of the scene in extreme conditions when compared to the state-of-the-art algorithms.",

keywords = "3D reconstruction, Bayesian inference, multispectral Lidar imaging, obscurants, robust estimation",

author = "Abderrahim Halimi and Jakeoung Koo and Lamb, \{Robert A.\} and Buller, \{Gerald Stuart\} and Stephen McLaughlin",

note = "Funding Information: This work was supported by the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF/201718/17128), EP-SRC Grants EP/T00097X/1,EP/S000631/1,EP/S026428/1, the Dasa project DSTLX1000147844. Publisher Copyright: {\textcopyright} 2022 IEEE; IEEE International Conference on Acoustics, Speech and Signal Processing 2022, IEEE ICASSP 2022 ; Conference date: 22-05-2022 Through 27-05-2022",

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Halimi, A, Koo, J, Lamb, RA, Buller, GS & McLaughlin, S 2022, Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background. in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp. 1531-1535, IEEE International Conference on Acoustics, Speech and Signal Processing 2022, Singapore, 22/05/22. https://doi.org/10.1109/ICASSP43922.2022.9746166

Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background. / Halimi, Abderrahim; Koo, Jakeoung; Lamb, Robert A. et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2022. p. 1531-1535.

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

TY - GEN

T1 - Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background

AU - Halimi, Abderrahim

AU - Koo, Jakeoung

AU - Lamb, Robert A.

AU - Buller, Gerald Stuart

AU - McLaughlin, Stephen

N1 - Funding Information: This work was supported by the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF/201718/17128), EP-SRC Grants EP/T00097X/1,EP/S000631/1,EP/S026428/1, the Dasa project DSTLX1000147844. Publisher Copyright: © 2022 IEEE

PY - 2022/4/27

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N2 - This paper presents a new Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data ac- quired in extreme conditions. We focus on imaging through obscurants (i.e., fog, water) leading to high and possibly non-uniform background noise. The proposed hierarchical Bayesian method accounts for multiscale information to pro- vide distribution estimates for the target’s depth and reflec- tivity, i.e., point and uncertainty measures of the estimates to improve decision making. The correlations between variables are enforced using a weighting scheme that allows the incor- poration of guide information available from other sensors or state-of-the-art algorithms. Results on synthetic and real data show improved reconstruction of the scene in extreme conditions when compared to the state-of-the-art algorithms.

AB - This paper presents a new Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data ac- quired in extreme conditions. We focus on imaging through obscurants (i.e., fog, water) leading to high and possibly non-uniform background noise. The proposed hierarchical Bayesian method accounts for multiscale information to pro- vide distribution estimates for the target’s depth and reflec- tivity, i.e., point and uncertainty measures of the estimates to improve decision making. The correlations between variables are enforced using a weighting scheme that allows the incor- poration of guide information available from other sensors or state-of-the-art algorithms. Results on synthetic and real data show improved reconstruction of the scene in extreme conditions when compared to the state-of-the-art algorithms.

KW - 3D reconstruction

KW - Bayesian inference

KW - multispectral Lidar imaging

KW - obscurants

KW - robust estimation

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M3 - Conference contribution

SP - 1531

EP - 1535

BT - ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing

PB - IEEE

T2 - IEEE International Conference on Acoustics, Speech and Signal Processing 2022

Y2 - 22 May 2022 through 27 May 2022

ER -

Robust Bayesian Reconstruction of Multispectral Single-Photon 3D Lidar Data with Non-Uniform Background

Abstract

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Keywords

ASJC Scopus subject areas

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