Event Stream Filtering via Probability Flux Estimation
This addresses noise and signal inconsistency issues in event cameras, which are critical for applications like robotics and high-speed vision, though it appears incremental by focusing on a novel method for a known bottleneck.
The paper tackles the problem of filtering noisy event camera data by modeling event generation as probability fluxes, resulting in high-fidelity denoising and motion reconstruction with real-time processing enabled by an O(1) recursive solver.
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.