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The stereo event-intensity camera setup is widely applied to leverage theadvantages of both event cameras with low latency and intensity cameras thatcapture accurate brightness and texture information. However, such a setupcommonly encounters cross-modality parallax that is difficult to be eliminatedsolely with stereo rectification especially for real-world scenes with complexmotions and varying depths, posing artifacts and distortion for existingEvent-based Video Frame Interpolation (E-VFI) approaches. To tackle thisproblem, we propose a novel Stereo Event-based VFI (SE-VFI) network (SEVFI-Net)to generate high-quality intermediate frames and corresponding disparities frommisaligned inputs consisting of two consecutive keyframes and event streamsemitted between them. Specifically, we propose a Feature Aggregation Module(FAM) to alleviate the parallax and achieve spatial alignment in the featuredomain. We then exploit the fused features accomplishing accurate optical flowand disparity estimation, and achieving better interpolated results throughflow-based and synthesis-based ways. We also build a stereo visual acquisitionsystem composed of an event camera and an RGB-D camera to collect a new StereoEvent-Intensity Dataset (SEID) containing diverse scenes with complex motionsand varying depths. Experiments on public real-world stereo datasets, i.e.,DSEC and MVSEC, and our SEID dataset demonstrate that our proposed SEVFI-Netoutperforms state-of-the-art methods by a large margin.

Video Frame Interpolation with Stereo Event and Intensity Camera