  By combining differentiable rendering with explicit point-based scene
representations, 3D Gaussian Splatting (3DGS) has demonstrated breakthrough 3D
reconstruction capabilities. However, to date 3DGS has had limited impact on
robotics, where high-speed egomotion is pervasive: Egomotion introduces motion
blur and leads to artifacts in existing frame-based 3DGS reconstruction
methods. To address this challenge, we introduce Event3DGS, an {\em
event-based} 3DGS framework. By exploiting the exceptional temporal resolution
of event cameras, Event3GDS can reconstruct high-fidelity 3D structure and
appearance under high-speed egomotion. Extensive experiments on multiple
synthetic and real-world datasets demonstrate the superiority of Event3DGS
compared with existing event-based dense 3D scene reconstruction frameworks;
Event3DGS substantially improves reconstruction quality (+3dB) while reducing
computational costs by 95\%. Our framework also allows one to incorporate a few
motion-blurred frame-based measurements into the reconstruction process to
further improve appearance fidelity without loss of structural accuracy.
