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一个光子事件驱动的3D成像

Alex Vicente Sola¹, Matthias Aquilina², Paul Kirkland^3,4

¹Neuromorphic Sensor Signal Processing Lab, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK.

Communications engineering

|November 29, 2025

概括

这项研究引入了新的光子驱动基于事件的成像,用于高速3D传感. 这种新方法可以通过处理单个光子到达来实现低光,低延迟成像,用于像LiDAR这样的应用.

科学领域:

光子学和计算成像技术
人工智能在感知中的作用
基于事件的高速系统.

背景情况:

传统的基于事件的成像依赖于强度变化,限制其在低光或单光子应用中的使用.
现有的方法在微秒级事件检测方面扎,阻碍了像单光子LiDAR和低光照相等应用.
需要能够在单光子水平上进行异步,低延迟检测的成像模式.

研究的目的:

开发一种由单个光子检测事件驱动的新成像模式.
为了在具有挑战性的低光条件下实现高速,低延迟的3D成像和传感.
利用尖端卷积神经网络 (SCNN) 来从光子到达时间进行异步图像重建.

主要方法:

实施了一种单像素成像方法,将3D场景信息编码为光子到达时间.
使用尖端卷积神经网络 (SCNN) 进行异步图像重建.
开发了一种光子驱动的事件检测系统,用于实时数据处理.

主要成果:

展示了一个由单个光子检测事件驱动的成像模式.
使用SCNNs实现异步图像重建,处理光子到达时间.
展示了最小延迟3D成像和弱光感应的潜力.

结论:

拟议的光子驱动的基于事件的成像模式克服了传统基于强度的方法的局限性.
SCNN为单光子检测提供了一个自然的,非同步的处理框架.
这种方法为高速目标检测,机器人技术和低光3D传感应用提供了巨大的潜力.

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