  Event cameras are novel sensors that output brightness changes in the form of
a stream of asynchronous events instead of intensity frames. Compared to
conventional image sensors, they offer significant advantages: high temporal
resolution, high dynamic range, no motion blur, and much lower bandwidth.
Recently, learning-based approaches have been applied to event-based data, thus
unlocking their potential and making significant progress in a variety of
tasks, such as monocular depth prediction. Most existing approaches use
standard feed-forward architectures to generate network predictions, which do
not leverage the temporal consistency presents in the event stream. We propose
a recurrent architecture to solve this task and show significant improvement
over standard feed-forward methods. In particular, our method generates dense
depth predictions using a monocular setup, which has not been shown previously.
We pretrain our model using a new dataset containing events and depth maps
recorded in the CARLA simulator. We test our method on the Multi Vehicle Stereo
Event Camera Dataset (MVSEC). Quantitative experiments show up to 50%
improvement in average depth error with respect to previous event-based
methods.
