Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data

Sandor Plosz; Istvan Gyongy; Jonathan Leach; Stephen McLaughlin; Gerald Stuart Buller; Abderrahim Halimi

doi:10.1109/icassp49357.2023.10095844

Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data

Sandor Plosz, Istvan Gyongy, Jonathan Leach, Stephen McLaughlin, Gerald Stuart Buller, Abderrahim Halimi

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

1 Citation (Scopus)

137 Downloads (Pure)

Abstract

Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.

Original language	English
Title of host publication	2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
ISBN (Electronic)	9781728163277
DOIs	https://doi.org/10.1109/icassp49357.2023.10095844
Publication status	Published - 5 May 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023 - Rhodes Island, Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 Conference number: 48 https://2023.ieeeicassp.org/ https://2023.ieeeicassp.org

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023
Abbreviated title	ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23
Internet address	https://2023.ieeeicassp.org/ https://2023.ieeeicassp.org

Keywords

3D reconstruction
GPUs
Lidar
Poisson noise
parallel coding
real-time estimation

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/icassp49357.2023.10095844

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Cite this

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title = "Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data",

abstract = "Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.",

keywords = "3D reconstruction, GPUs, Lidar, Poisson noise, parallel coding, real-time estimation",

author = "Sandor Plosz and Istvan Gyongy and Jonathan Leach and Stephen McLaughlin and Buller, {Gerald Stuart} and Abderrahim Halimi",

year = "2023",

month = may,

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booktitle = "2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

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note = "48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

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Plosz, S, Gyongy, I, Leach, J , McLaughlin, S , Buller, GS & Halimi, A 2023, Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data. in 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., 10095844, IEEE, 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/icassp49357.2023.10095844

TY - GEN

T1 - Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data

AU - Plosz, Sandor

AU - Gyongy, Istvan

AU - Leach, Jonathan

AU - McLaughlin, Stephen

AU - Buller, Gerald Stuart

AU - Halimi, Abderrahim

N1 - Conference code: 48

PY - 2023/5/5

Y1 - 2023/5/5

N2 - Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.

AB - Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.

KW - 3D reconstruction

KW - GPUs

KW - Lidar

KW - Poisson noise

KW - parallel coding

KW - real-time estimation

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DO - 10.1109/icassp49357.2023.10095844

M3 - Conference contribution

BT - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023

Y2 - 4 June 2023 through 10 June 2023

ER -

Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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