  Event cameras are considered to have great potential for computer vision and
robotics applications because of their high temporal resolution and low power
consumption characteristics. However, the event stream output from event
cameras has asynchronous, sparse characteristics that existing computer vision
algorithms cannot handle. Spiking neural network is a novel event-based
computational paradigm that is considered to be well suited for processing
event camera tasks. However, direct training of deep SNNs suffers from
degradation problems. This work addresses these problems by proposing a spiking
neural network architecture with a novel residual block designed and
multi-dimension attention modules combined, focusing on the problem of depth
prediction. In addition, a novel event stream representation method is
explicitly proposed for SNNs. This model outperforms previous ANN networks of
the same size on the MVSEC dataset and shows great computational efficiency.
