  The stereo event-intensity camera setup is widely applied to leverage the
advantages of both event cameras with low latency and intensity cameras that
capture accurate brightness and texture information. However, such a setup
commonly encounters cross-modality parallax that is difficult to be eliminated
solely with stereo rectification especially for real-world scenes with complex
motions and varying depths, posing artifacts and distortion for existing
Event-based Video Frame Interpolation (E-VFI) approaches. To tackle this
problem, we propose a novel Stereo Event-based VFI (SE-VFI) network (SEVFI-Net)
to generate high-quality intermediate frames and corresponding disparities from
misaligned inputs consisting of two consecutive keyframes and event streams
emitted between them. Specifically, we propose a Feature Aggregation Module
(FAM) to alleviate the parallax and achieve spatial alignment in the feature
domain. We then exploit the fused features accomplishing accurate optical flow
and disparity estimation, and achieving better interpolated results through
flow-based and synthesis-based ways. We also build a stereo visual acquisition
system composed of an event camera and an RGB-D camera to collect a new Stereo
Event-Intensity Dataset (SEID) containing diverse scenes with complex motions
and varying depths. Experiments on public real-world stereo datasets, i.e.,
DSEC and MVSEC, and our SEID dataset demonstrate that our proposed SEVFI-Net
outperforms state-of-the-art methods by a large margin.
