E-3DGS: Event-based Novel View Rendering of Large-scale Scenes Using 3D Gaussian Splatting

Existing novel view synthesis techniques predominantly utilize RGB cameras, inheriting their limitations such as the need for sufficient lighting, susceptibility to motion blur, and restricted dynamic range. In contrast, event cameras, which are impervious to these limitations, have seen limited exploration in this domain, particularly in large-scale settings. Current methodologies primarily focus on front-facing or object-oriented (360-degree view) scenarios. For the first time, we introduce 3D Gaussians for event-based novel view synthesis. Our method allows to reconstruct high-quality large and unbounded scenes. We contribute the first real and synthetic event datasets tailored for this setting. Our method demonstrates superior novel view synthesis and consistently outperforms the baseline EventNeRF by a margin of 11-25% in PSNR (dB) while being orders of magnitude faster in reconstruction and rendering.