Self-supervised Event-based Monocular Depth Estimation using Cross-modal
  Consistency
Junyu Zhu Lina Liu Bofeng Jiang Feng Wen Hongbo Zhang Wanlong Li Yong Liu
An event camera is a novel vision sensor that can capture per-pixel
brightness changes and output a stream of asynchronous ``events''. It has
advantages over conventional cameras in those scenes with high-speed motions
and challenging lighting conditions because of the high temporal resolution,
high dynamic range, low bandwidth, low power consumption, and no motion blur.
Therefore, several supervised monocular depth estimation from events is
proposed to address scenes difficult for conventional cameras. However, depth
annotation is costly and time-consuming. In this paper, to lower the annotation
cost, we propose a self-supervised event-based monocular depth estimation
framework named EMoDepth. EMoDepth constrains the training process using the
cross-modal consistency from intensity frames that are aligned with events in
the pixel coordinate. Moreover, in inference, only events are used for
monocular depth prediction. Additionally, we design a multi-scale
skip-connection architecture to effectively fuse features for depth estimation
while maintaining high inference speed. Experiments on MVSEC and DSEC datasets
demonstrate that our contributions are effective and that the accuracy can
outperform existing supervised event-based and unsupervised frame-based
methods.