Parametric Modeling and Estimation of Photon Registrations for 3D Imaging

TL;DR

This paper introduces a continuous parametric model for photon registration in SP-LiDAR, enabling faster and more accurate depth estimation by overcoming computational challenges of previous discrete models.

Contribution

The paper proposes a Gaussian-uniform mixture model and a tailored EM algorithm to improve histogram modeling in photon detection, facilitating neural network integration and rapid simulations.

Findings

Achieved accurate histogram matching in challenging scenarios.

Reduced computational complexity compared to discrete models.

Enhanced depth estimation precision in SP-LiDAR systems.

Abstract

In single-photon light detection and ranging (SP-LiDAR) systems, the histogram distortion due to hardware dead time fundamentally limits the precision of depth estimation. To compensate for the dead time effects, the photon registration distribution is typically modeled based on the Markov chain self-excitation process. However, this is a discrete process and it is computationally expensive, thus hindering potential neural network applications and fast simulations. In this paper, we overcome the modeling challenge by proposing a continuous parametric model. We introduce a Gaussian-uniform mixture model (GUMM) and periodic padding to address high noise floors and noise slopes respectively. By deriving and implementing a customized expectation maximization (EM) algorithm, we achieve accurate histogram matching in scenarios that were deemed difficult in the literature.