Event cameras are bio-inspired sensors with high dynamic range, high temporal resolution, and low power consumption. These features enable precise motion detection even in challenging lighting conditions and fast-changing scenes, rendering them well-suited for optical flow estimation. However, event camera output is sparse and unstructured, making it challenging to process. Transformer architectures have shown to be effective in capturing long-term temporal dependencies and processing sparse input, hence they might be better suited to processing this output by leveraging the fine time granularity inherent to event camera data.
We introduce E-GMFlow, an approach for event-based optical flow inspired by the recent success in terms of accuracy of transformer-based models for frame-based optical flow. We explore the effect of temporal details on the accuracy of this transformer architecture by changing the number of temporal bins in which events are discretized. We observe that the increase in the number of temporal bins generally causes higher accuracy and comment on the limitations of this study.