  Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence
of pixels subject to brightness changes. Differently from classic vision
devices, they produce a sparse representation of the scene. Therefore, to apply
standard computer vision algorithms, events need to be integrated into a frame
or event-surface. This is usually attained through hand-crafted grids that
reconstruct the frame using ad-hoc heuristics. In this paper, we propose
Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells that efficiently
process events and learn end-to-end task-dependent event-surfaces. Compared to
existing reconstruction approaches, our learned event-surface shows good
flexibility and expressiveness on optical flow estimation on the MVSEC
benchmark and it improves the state-of-the-art of event-based object
classification on the N-Cars dataset.
