Title: Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping Show Chinese title

Title: 基于神经映射的超快高通量单光子激光雷达仿真器

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. Show Chinese abstract

Abstract: 单光子激光雷达（SPL）中光子注册的高效模拟对于诸如在高通量条件下进行深度估计等应用至关重要，在这种情况下，硬件死时间会显著扭曲光子测量。 然而，由于其固有的逐光子顺序处理特性，传统方法计算开销巨大。 本文提出了一种基于学习的框架，通过使用自动编码器（AE）对光子计数进行建模并直接预测光子注册概率密度函数（PDF），从而加速模拟过程。 我们的方法在估算注册光子总数及其时间分布方面实现了高度准确性，同时大幅减少了模拟时间。 广泛的实验验证了我们方法的有效性和效率，突显了它在高通量条件下快速准确地进行SPL模拟以支持数据密集型成像任务的巨大潜力。