Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data

Julián Tachella, Yoann Altmann, Ximing Ren, Aongus McCarthy, Gerald Stuart Buller, Stephen McLaughlin, Jean-Yves Tourneret

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)
278 Downloads (Pure)


Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. In a general setting, more than one surface can be observed in a single pixel. The problem of estimating the number of surfaces, their reflectivity and position becomes very challenging in the low-photon regime (which equates to short acquisition times) or relatively high background levels (i.e., strong ambient illumination). This paper presents a new approach to 3D reconstruction using single-photon, single-wavelength Lidar data, which is capable of identifying multiple surfaces in each pixel. Adopting a Bayesian approach, the 3D structure to be recovered is modelled as a marked point process and reversible jump Markov chain Monte Carlo (RJ-MCMC) moves are proposed to sample the posterior distribution of interest. In order to promote spatial correlation between points belonging to the same surface, we propose a prior that combines an area interaction process and a Strauss process. New RJ-MCMC dilation and erosion updates are presented to achieve an efficient exploration of the configuration space. To further reduce the computational load, we adopt a multiresolution approach, processing the data from a coarse to the finest scale. The experiments performed with synthetic and real data show that the algorithm obtains better reconstructions than other recently published optimization algorithms for lower execution times.
Original languageEnglish
Pages (from-to)521-550
Number of pages30
JournalSIAM Journal on Imaging Sciences
Issue number1
Publication statusPublished - 14 Mar 2019


  • 3D reconstruction
  • Bayesian inference
  • Lidar
  • Low-photon imaging
  • Poisson noise

ASJC Scopus subject areas

  • General Mathematics
  • Applied Mathematics


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