Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras

Event cameras have recently shown promising capabilities ininstantaneous motion estimation due to their robustness to low light andfast motions. However, computing wide-baseline correspondence betweentwo arbitrary views remains a significant challenge, since event appear-ance changes substantially with motion, and learning-based approachesare constrained by both scalability and limited wide-baseline supervi-sion. We therefore introduce the first event matching model that achievescross-dataset wide-baseline correspondence in a zero-shot manner:a single model trained once is deployed on unseen datasets withoutany target-domain fine-tuning or adaptation. To enable this capabil-ity, we introduce a motion-robust and computationally efficient atten-tion backbone that learns multi-timescale features from event streams,augmented with sparsity-aware event token selection, making large-scaletraining on diverse wide-baseline supervision computationally feasible.To provide the supervision needed for wide-baseline generalization, wedevelop a robust event motion synthesis framework to generate large-scale event-matching datasets with augmented viewpoints, modalities,and motions. Extensive experiments across multiple benchmarks showthat our framework achieves a 37.7% improvement over the previousbest event feature matching methods. Code and data are available at:https: // github. com/ spikelab-jhu/ Match-Any-Events .