作为概率事件的量子感知
Quanta Perception as Probabilistic Events
浏览论文内容
中文总结 AI 辅助
本文提出概率事件计算原语，通过递归贝叶斯推理将单个光子检测转化为低延迟信号，实现极端环境下的量子感知，其输出速度远超现有基线，连接了量子传感与机器人视觉。
中文摘要 AI 辅助
自主系统依赖从光中提取信息，但在夜间导航、高速机器人等极端环境中仍存在脆弱性。传统传感器在固定曝光时间内聚合光子，在灵敏度、动态范围和时间分辨率之间施加了权衡，当光子稀少或动态快速时会降低感知性能。量子传感器可检测单个光子，但其数据流超出实时计算和延迟预算数个数量级。本文提出概率事件，一种用于从单个光子检测中实现实时量子感知的计算原语。通过计算自上次强度变化以来的后验概率，将光子流表示为递归信念状态。与固定阈值的事件相机触发器不同，该递归贝叶斯公式产生三种低延迟信号：运动自适应场景通量、高保真活动图和基于熵的感知不确定性。该表示可在极端条件下实现感知，包括在约0.05 lux环境中对跑步者的位姿估计——无需重新训练视觉模型。本文方法在商用GPU硬件上处理超过50000帧每秒的量子输入流，产生千赫兹级输出，比最先进的量子重建基线快四个数量级，即使对于百万像素阵列也是如此。通过用对光子流的直接概率推理替代帧重建，本研究将光子计数量子传感与机器人视觉联系起来。
英文摘要
Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed robotics. Conventional sensors aggregate photons over fixed exposures, imposing trade-offs between sensitivity, dynamic range, and temporal resolution that degrade perception when photons are scarce or dynamics are rapid. Quanta sensors detect individual photons, but their streams exceed real-time compute and latency budgets by orders of magnitude. Here we introduce $\textit{probabilistic events}$, a computational primitive for real-time quanta perception from individual photon detections. By computing the posterior over the time since the last intensity change, we represent photon streams as recursive belief states. Rather than fixed-threshold event-camera triggers, this recursive Bayesian formulation yields three low-latency signals: motion-adaptive scene flux, high-fidelity activity maps, and entropy-based perceptual uncertainty. This representation enables perception in extreme conditions, including pose estimation of a running person at $\sim$0.05 lux---without retraining vision models. Our approach processes input streams exceeding 50{,}000 quanta frames per second on commodity GPU hardware---yielding kilohertz-scale outputs up to four orders of magnitude faster than state-of-the-art quanta reconstruction baselines, even for megapixel arrays. By replacing frame reconstruction with direct probabilistic inference over photon streams, this work bridges photon-counting quanta sensing with robotic vision.
发表机构
- University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。
