Expectation-Maximization based approach to 3D reconstruction from single-waveform multispectral Lidar data

Quentin Legros, Sylvain Meignen, Stephen McLaughlin, Yoann Altmann

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
44 Downloads (Pure)


In this article, we present a novel Bayesian approach for estimating spectral and range profiles from single-photon Lidar waveforms associated with single surfaces in the photon-limited regime. In contrast to classical multispectral Lidar signals, we consider a single Lidar waveform per pixel, whereby a single detector is used to acquire information simultaneously at multiple wavelengths. A new observation model based on a mixture of distributions is developed. It relates the unknown parameters of interest to the observed waveforms containing information from multiple wavelengths. Adopting a Bayesian approach, several prior models are investigated and a stochastic Expectation-Maximization algorithm is proposed to estimate the spectral and depth profiles. The reconstruction performance and computational complexity of our approach are assessed, for different prior models, through a series of experiments using synthetic and real data under different observation scenarios. The results obtained demonstrate a significant speed-up (up to 100 times faster for four bands) without significant degradation of the reconstruction performance when compared to existing methods in the photon-starved regime.

Original languageEnglish
Pages (from-to)1033-1043
Number of pages11
JournalIEEE Transactions on Computational Imaging
Early online date29 May 2020
Publication statusPublished - 2020


  • 3D imaging
  • Bayesian estimation
  • Expectation-Maximization
  • Multispectral imaging
  • singlephoton Lidar

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computational Mathematics


Dive into the research topics of 'Expectation-Maximization based approach to 3D reconstruction from single-waveform multispectral Lidar data'. Together they form a unique fingerprint.

Cite this