Quanta Perception as Probabilistic Events

• URL: http://arxiv.org/abs/2608.27584v1
• Date: Thu, 27 Aug 2026 18:10:04 GMT
• Title: Quanta Perception as Probabilistic Events
• Abstract summary: Quanta sensors detect individual photons, but their streams exceed real-time compute and latency budgets by orders of magnitude.<n>Here we introduce $textitprobabilistic events$, a computational primitive for real-time quanta perception from individual photon detections.<n>By replacing frame reconstruction with direct probabilistic inference over photon streams, this work bridges photon-counting quanta sensing with robotic vision.
• Score: 10.195740497918049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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