Motion Before Events: IMU-Guided Optical Control and Timestamp-Aware Compensation

Neuromorphic devices like event cameras enable low-latency visual sensing, but their information content depends strongly on image motion. Weak motion may leave important structures under-observed, whereas strong motion can produce excessive and redundant events. Inspired by active modulation in biological vision, we present Event-Inertial Retinal Modulation (EIRM), a bio-inspired framework that adaptively regulates optical excitation according to the current sensing state. EIRM uses a causal temporal policy to select discrete wedge-prism rotation speeds from recent visual, inertial, and event-derived observations, increasing modulation when natural motion is insufficient and reducing it when event excitation is already adequate. A complementary rule-based feedback loop regulates controlled camera motion from aligned-event activity. To assess whether the resulting events are informative rather than merely abundant, we introduce Target Event Observability (TEO), which jointly measures structural coverage, event purity, and temporal persistence. We further construct a hybrid benchmark combining controllable synthetic scenes, geometry-consistent reconstructions, and real-world visual–inertial trajectories. Experiments show that EIRM recovers useful structure under weak motion while suppressing unnecessary excitation under strong motion.