基于概率通量估计的事件流滤波
Event Stream Filtering via Probability Flux Estimation
把事件流建模为辐照度轨迹随机扩散产生的阈值穿越概率通量，用核方法做非参数估计并以 O(1) 递归求解器实时重建连续事件密度流，可作为事件相机前端去噪模块直接接入下游重建或运动估计流程。
原文依据 EDFilter performs nonparametric, kernel-based estimation of probability flux and reconstructs the continuous event density flow using an O(1) recursive solver
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论事件相机以微秒级延迟异步捕捉亮度变化，具有出色的时序精度，但受严重噪声和信号不一致性困扰。与传统信号不同，事件通过极性携带状态信息，并通过事件间时间间隔处理信息。然而，现有事件滤波器往往忽略后者，产生的输出比原始输入更稀疏，限制了连续辐照度动态的重建。我们提出事件密度流滤波器（EDFilter），该框架将事件生成建模为辐照度轨迹随机扩散所产生的阈值穿越概率通量。EDFilter 对概率通量进行非参数、基于核的估计，并使用 O(1) 递归求解器重建连续事件密度流，从而实现实时处理。本文还提出了旋转事件数据集（RED），其在受控光照下具有微秒分辨率的真值辐照度流，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。
英文原文摘要 1192 字符
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.