用概率通量建模事件流，实现实时高保真去噪与运动重建。
Event Stream Filtering via Probability Flux Estimation
- 将事件生成视为亮度轨迹的随机扩散通量，非参数估计概率流。
- 通过常数时间递归求解器重构连续事件密度流，支持实时处理。
- 提出微秒级真实光照数据集，适合需要高精度事件处理的研究者。
事件相机以微秒级延迟异步捕捉亮度变化，具备极高的时间精度，但存在严重噪声和信号不一致问题。与传统信号不同，事件通过极性携带状态信息，通过事件间时间间隔传递过程信息。然而，现有事件滤波器常忽略后者，导致输出比原始输入更稀疏，限制了连续辐射度动态的重建。本文提出事件密度流滤波器（EDFilter），将事件生成建模为由辐射度轨迹随机扩散引起的阈值穿越概率通量。EDFilter采用非参数核估计方法对概率通量进行估计，并使用O(1)递归求解器重构连续事件密度流，实现实时处理。同时，提出了旋转事件数据集（RED），在受控光照下提供微秒级真实辐射度流标注，用于事件质量评估。实验表明，EDFilter实现了高保真、物理可解释的事件去噪与运动重建。
原文摘要 · Abstract (English)
Event cameras asynchronously capture brightness changes with microsecond latency, offering exceptional temporal precision but suffering from severe noise and signal inconsistencies. Unlike conventional signals, events carry state information through polarities and process information through inter-event time intervals. However, existing event filters often ignore the latter, producing outputs that are sparser than the raw input and limiting the reconstruction of continuous irradiance dynamics. We propose the Event Density Flow Filter (EDFilter), a framework that models event generation as threshold-crossing probability fluxes arising from the stochastic diffusion of irradiance trajectories. EDFilter performs nonparametric, kernel-based estimation of probability flux and reconstructs the continuous event density flow using an O(1) recursive solver, enabling real-time processing. The Rotary Event Dataset (RED), featuring microsecond-resolution ground-truth irradiance flow under controlled illumination is also presented for event quality evaluation. Experiments demonstrate that EDFilter achieves high-fidelity, physically interpretable event denoising and motion reconstruction.
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