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

TL;DR

This paper introduces a Bayesian method for 3D scene reconstruction from single-photon Lidar data, capable of detecting multiple surfaces per pixel even in low-photon and high-background conditions.

Contribution

It develops a novel Bayesian framework with a marked point process model and RJ-MCMC sampling, incorporating spatial priors and a multiresolution approach for efficient, accurate 3D reconstruction.

Findings

Outperforms recent optimization algorithms in accuracy.

Achieves better reconstructions with lower execution times.

Effective in low-photon and high-background scenarios.

Abstract

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.