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Event cameras provide high temporal precision, low data rates, and highdynamic range visual perception, which are well-suited for optical flowestimation. While data-driven optical flow estimation has obtained greatsuccess in RGB cameras, its generalization performance is seriously hindered inevent cameras mainly due to the limited and biased training data. In thispaper, we present a novel simulator, BlinkSim, for the fast generation oflarge-scale data for event-based optical flow. BlinkSim consists of aconfigurable rendering engine and a flexible engine for event data simulation.By leveraging the wealth of current 3D assets, the rendering engine enables usto automatically build up thousands of scenes with different objects, textures,and motion patterns and render very high-frequency images for realistic eventdata simulation. Based on BlinkSim, we construct a large training dataset andevaluation benchmark BlinkFlow that contains sufficient, diversiform, andchallenging event data with optical flow ground truth. Experiments show thatBlinkFlow improves the generalization performance of state-of-the-art methodsby more than 40% on average and up to 90%. Moreover, we further propose anEvent optical Flow transFormer (E-FlowFormer) architecture. Powered by ourBlinkFlow, E-FlowFormer outperforms the SOTA methods by up to 91% on MVSECdataset and 14% on DSEC dataset and presents the best generalizationperformance.

BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation