  Novel View Synthesis plays a crucial role by generating new 2D renderings
from multi-view images of 3D scenes. However, capturing high-speed scenes with
conventional cameras often leads to motion blur, hindering the effectiveness of
3D reconstruction. To address this challenge, high-frame-rate dense 3D
reconstruction emerges as a vital technique, enabling detailed and accurate
modeling of real-world objects or scenes in various fields, including Virtual
Reality or embodied AI. Spike cameras, a novel type of neuromorphic sensor,
continuously record scenes with an ultra-high temporal resolution, showing
potential for accurate 3D reconstruction. Despite their promise, existing
approaches, such as applying Neural Radiance Fields (NeRF) to spike cameras,
encounter challenges due to the time-consuming rendering process. To address
this issue, we make the first attempt to introduce the 3D Gaussian Splatting
(3DGS) into spike cameras in high-speed capture, providing 3DGS as dense and
continuous clues of views, then constructing SpikeGS. Specifically, to train
SpikeGS, we establish computational equations between the rendering process of
3DGS and the processes of instantaneous imaging and exposing-like imaging of
the continuous spike stream. Besides, we build a very lightweight but effective
mapping process from spikes to instant images to support training. Furthermore,
we introduced a new spike-based 3D rendering dataset for validation. Extensive
experiments have demonstrated our method possesses the high quality of novel
view rendering, proving the tremendous potential of spike cameras in modeling
3D scenes.
