  Depth from Focus estimates depth by determining the moment of maximum focus
from multiple shots at different focal distances, i.e. the Focal Stack.
However, the limited sampling rate of conventional optical cameras makes it
difficult to obtain sufficient focus cues during the focal sweep. Inspired by
biological vision, the event camera records intensity changes over time in
extremely low latency, which provides more temporal information for focus time
acquisition. In this study, we propose the EDFF Network to estimate sparse
depth from the Event Focal Stack. Specifically, we utilize the event voxel grid
to encode intensity change information and project event time surface into the
depth domain to preserve per-pixel focal distance information. A
Focal-Distance-guided Cross-Modal Attention Module is presented to fuse the
information mentioned above. Additionally, we propose a Multi-level Depth
Fusion Block designed to integrate results from each level of a UNet-like
architecture and produce the final output. Extensive experiments validate that
our method outperforms existing state-of-the-art approaches.
