  Computational neuromorphic imaging (CNI) with event cameras offers advantages
such as minimal motion blur and enhanced dynamic range, compared to
conventional frame-based methods. Existing event-based radiance field rendering
methods are built on neural radiance field, which is computationally heavy and
slow in reconstruction speed. Motivated by the two aspects, we introduce Ev-GS,
the first CNI-informed scheme to infer 3D Gaussian splatting from a monocular
event camera, enabling efficient novel view synthesis. Leveraging 3D Gaussians
with pure event-based supervision, Ev-GS overcomes challenges such as the
detection of fast-moving objects and insufficient lighting. Experimental
results show that Ev-GS outperforms the method that takes frame-based signals
as input by rendering realistic views with reduced blurring and improved visual
quality. Moreover, it demonstrates competitive reconstruction quality and
reduced computing occupancy compared to existing methods, which paves the way
to a highly efficient CNI approach for signal processing.
