Temporal Stereo Matching From Event Cameras via Joint Learning With Stereoscopic Flow.

Abstract

Event cameras are dynamic vision sensors inspired by the biological retina, offering high dynamic range, high temporal resolution, and low power consumption. These qualities allow them to perceive 3D environments even in extreme conditions. Event data is continuously recorded over time, capturing pixel movements in detail. To leverage this temporal density, we introduce a temporal event stereo framework that continuously uses past information. The event stereo matching network is jointly trained with stereoscopic flow, which tracks pixel movements from stereo cameras. Instead of relying on optical flow ground truth, our method trains motion flows using disparity maps. The temporal aggregation of information via stereoscopic flow boosts stereo matching performance, achieving state-of-the-art results on MVSEC, DSEC, M3ED, and EVIMO2 datasets. Our method also demonstrates computational efficiency by stacking past data in a cascading manner.