  Event cameras, offering high temporal resolutions and high dynamic ranges,
have brought a new perspective to address common challenges (e.g., motion blur
and low light) in monocular depth estimation. However, how to effectively
exploit the sparse spatial information and rich temporal cues from asynchronous
events remains a challenging endeavor. To this end, we propose a novel
event-based monocular depth estimator with recurrent transformers, namely
EReFormer, which is the first pure transformer with a recursive mechanism to
process continuous event streams. Technically, for spatial modeling, a novel
transformer-based encoder-decoder with a spatial transformer fusion module is
presented, having better global context information modeling capabilities than
CNN-based methods. For temporal modeling, we design a gate recurrent vision
transformer unit that introduces a recursive mechanism into transformers,
improving temporal modeling capabilities while alleviating the expensive GPU
memory cost. The experimental results show that our EReFormer outperforms
state-of-the-art methods by a margin on both synthetic and real-world datasets.
We hope that our work will attract further research to develop stunning
transformers in the event-based vision community. Our open-source code can be
found in the supplemental material.
