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

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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 languageEnglish
Title of host publicationICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Pages1531-1535
Number of pages5
ISBN (Electronic)9781665405409
DOIs
Publication statusPublished - 27 Apr 2022
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2022 - , Singapore
Duration: 22 May 202227 May 2022
https://2022.ieeeicassp.org/

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2022
Abbreviated titleIEEE ICASSP 2022
Country/TerritorySingapore
Period22/05/2227/05/22
Internet address

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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