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In this work, we propose a novel framework for unsupervised learning forevent cameras that learns motion information from only the event stream. Inparticular, we propose an input representation of the events in the form of adiscretized volume that maintains the temporal distribution of the events,which we pass through a neural network to predict the motion of the events.This motion is used to attempt to remove any motion blur in the event image. Wethen propose a loss function applied to the motion compensated event image thatmeasures the motion blur in this image. We train two networks with thisframework, one to predict optical flow, and one to predict egomotion anddepths, and evaluate these networks on the Multi Vehicle Stereo Event Cameradataset, along with qualitative results from a variety of different scenes.

Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion