Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping

TL;DR

This paper introduces a neural network-based simulator for single-photon LiDAR that significantly speeds up photon registration modeling under high-flux conditions, maintaining high accuracy and reducing computational costs.

Contribution

It presents a novel learning-based framework using an autoencoder to efficiently simulate photon registration in SPL, outperforming traditional methods in speed and accuracy.

Findings

High accuracy in photon count and temporal distribution estimation

Substantial reduction in simulation time

Validated effectiveness through extensive experiments

Abstract

Efficient simulation of photon registrations in single-photon LiDAR (SPL) is essential for applications such as depth estimation under high-flux conditions, where hardware dead time significantly distorts photon measurements. However, the conventional wisdom is computationally intensive due to their inherently sequential, photon-by-photon processing. In this paper, we propose a learning-based framework that accelerates the simulation process by modeling the photon count and directly predicting the photon registration probability density function (PDF) using an autoencoder (AE). Our method achieves high accuracy in estimating both the total number of registered photons and their temporal distribution, while substantially reducing simulation time. Extensive experiments validate the effectiveness and efficiency of our approach, highlighting its potential to enable fast and accurate SPL simulations for data-intensive imaging tasks in the high-flux regime.