Event cameras are novel vision sensors that sample, in an asynchronous
fashion, brightness increments with low latency and high temporal resolution.
The resulting streams of events are of high value by themselves, especially for
high speed motion estimation. However, a growing body of work has also focused
on the reconstruction of intensity frames from the events, as this allows
bridging the gap with the existing literature on appearance- and frame-based
computer vision. Recent work has mostly approached this problem using neural
networks trained with synthetic, ground-truth data. In this work we approach,
for the first time, the intensity reconstruction problem from a self-supervised
learning perspective. Our method, which leverages the knowledge of the inner
workings of event cameras, combines estimated optical flow and the event-based
photometric constancy to train neural networks without the need for any
ground-truth or synthetic data. Results across multiple datasets show that the
performance of the proposed self-supervised approach is in line with the
state-of-the-art. Additionally, we propose a novel, lightweight neural network
for optical flow estimation that achieves high speed inference with only a
minor drop in performance.

该论文通过自监督学习，将基于事件相机的图像强度重构问题与光流估计相结合，避免了需要真实数据的问题，并提出了一种轻量化的神经网络，以实现快速推理和高精度的光流估计。