用神经网络加速单光子激光雷达仿真，提升高通量场景下速度与精度。
Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping
- 基于自编码器学习光子计数与注册概率分布，跳过逐光子计算。
- 仿真速度显著提升，总注册光子数与时间分布估计误差小。
- 适合需要快速生成高通量激光雷达数据的科研与工程应用。
单光子激光雷达（SPL）在高通量条件下，由于硬件死区效应导致光子测量严重失真，高效模拟光子注册过程至关重要。然而传统方法因逐光子顺序处理，计算开销大。本文提出一种基于学习的框架，通过自编码器（AE）建模光子计数并直接预测光子注册概率密度函数（PDF），避免了逐光子模拟。实验表明，该方法在准确估计注册光子总数及时间分布的同时，大幅缩短仿真时间，验证了其在高通量成像任务中实现快速、精准模拟的潜力。
原文摘要 · Abstract (English)
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.
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