  As an emerging vision sensor, the event camera has gained popularity in
various vision tasks such as optical flow estimation, stereo matching, and
depth estimation due to its high-speed, sparse, and asynchronous event streams.
Unlike traditional approaches that use specialized architectures for each
specific task, we propose a unified framework, EventMatch, that reformulates
these tasks as an event-based dense correspondence matching problem, allowing
them to be solved with a single model by directly comparing feature
similarities. By utilizing a shared feature similarities module, which
integrates knowledge from other event flows via temporal or spatial
interactions, and distinct task heads, our network can concurrently perform
optical flow estimation from temporal inputs (e.g., two segments of event
streams in the temporal domain) and stereo matching from spatial inputs (e.g.,
two segments of event streams from different viewpoints in the spatial domain).
Moreover, we further demonstrate that our unified model inherently supports
cross-task transfer since the architecture and parameters are shared across
tasks. Without the need for retraining on each task, our model can effectively
handle both optical flow and disparity estimation simultaneously. The
experiment conducted on the DSEC benchmark demonstrates that our model exhibits
superior performance in both optical flow and disparity estimation tasks,
outperforming existing state-of-the-art methods. Our unified approach not only
advances event-based models but also opens new possibilities for cross-task
transfer and inter-task fusion in both spatial and temporal dimensions. Our
code will be available later.
