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

TL;DR

This paper introduces a Bayesian method utilizing an EM algorithm for reconstructing spectral and depth profiles from single-waveform multispectral Lidar data, achieving faster results with maintained accuracy in photon-limited conditions.

Contribution

It presents a novel observation model and a Bayesian EM-based approach for spectral and range estimation from single-waveform multispectral Lidar data, improving speed and efficiency.

Findings

Significant speed-up over existing methods.

Maintained reconstruction accuracy in photon-starved conditions.

Effective performance demonstrated on synthetic and real data.

Abstract

In this paper, 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 without significant degradation of the reconstruction performance when compared to existing methods in the photon-starved regime.