Markov-Renewal Single-Photon LiDAR Simulator

TL;DR

This paper introduces a novel Markov-renewal process-based simulator for single-photon LiDAR that achieves high fidelity and speed, enabling large-scale data generation for learning-based reconstruction.

Contribution

It presents the first analytical model predicting photon count statistics in SP-LiDAR, combining accuracy with computational efficiency.

Findings

Achieves near-gold-standard fidelity in photon count simulation.

Orders of magnitude faster than traditional sequential models.

Enables large-scale, physically-faithful data generation for machine learning.

Abstract

Single-photon LiDAR (SP-LiDAR) simulators face a dilemma: fast but inaccurate Poisson models or accurate but prohibitively slow sequential models. This paper breaks that compromise. We present a simulator that achieves both fidelity and speed by focusing on the critical, yet overlooked, component of simulation: the photon count statistics. Our key contribution is a Markov-renewal process (MRP) formulation that, for the first time, analytically predicts the mean and variance of registered photon counts under dead time. To make this MRP model computationally tractable, we introduce a spectral truncation rule that efficiently computes the complex covariance statistics. By proving the shift-invariance of the process, we extend this per-pixel model to full histogram cube generation via a precomputed lookup table. Our method generates 3D cubes indistinguishable from the sequential gold-standard, yet is orders of magnitude faster. This finally enables large-scale, physically-faithful data generation for learning-based SP-LiDAR reconstruction.