Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals

Julián Tachella, Yoann Altmann, Miguel Márquez, Henry Arguello-Fuentes, Jean-Yves Tourneret, Stephen McLaughlin

Research output: Contribution to journalArticle

Abstract

Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions us- ing less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the ad- vantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.
Original languageEnglish
JournalIEEE Transactions on Computational Imaging
Publication statusAccepted/In press - 29 Aug 2019

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photons
acquisition
spectral bands
discrimination
pixels
sampling
degradation
requirements
estimates
lasers

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@article{62ff772a08334e8c8dc0109cca98452e,
title = "Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals",
abstract = "Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions us- ing less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the ad- vantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.",
author = "Juli{\'a}n Tachella and Yoann Altmann and Miguel M{\'a}rquez and Henry Arguello-Fuentes and Jean-Yves Tourneret and Stephen McLaughlin",
year = "2019",
month = "8",
day = "29",
language = "English",
journal = "IEEE Transactions on Computational Imaging",
issn = "2333-9403",
publisher = "IEEE",

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Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals. / Tachella, Julián; Altmann, Yoann; Márquez, Miguel; Arguello-Fuentes, Henry; Tourneret, Jean-Yves; McLaughlin, Stephen.

In: IEEE Transactions on Computational Imaging, 29.08.2019.

Research output: Contribution to journalArticle

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AU - Tachella, Julián

AU - Altmann, Yoann

AU - Márquez, Miguel

AU - Arguello-Fuentes, Henry

AU - Tourneret, Jean-Yves

AU - McLaughlin, Stephen

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AB - Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions us- ing less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the ad- vantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.

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