The recent emergence of 3D Gaussian splatting (3DGS) leverages the advantage
of explicit point-based representations, which significantly improves the
rendering speed and quality of novel-view synthesis. However, 3D radiance field
rendering in environments with high-dynamic motion or challenging illumination
condition remains problematic in real-world robotic tasks. The reason is that
fast egomotion is prevalent real-world robotic tasks, which induces motion
blur, leading to inaccuracies and artifacts in the reconstructed structure. To
alleviate this problem, we propose Event3DGS, the first method that learns
Gaussian Splatting solely from raw event streams. By exploiting the high
temporal resolution of event cameras and explicit point-based representation,
Event3DGS can reconstruct high-fidelity 3D structures solely from the event
streams under fast egomotion. Our sparsity-aware sampling and progressive
training approaches allow for better reconstruction quality and consistency. To
further enhance the fidelity of appearance, we explicitly incorporate the
motion blur formation process into a differentiable rasterizer, which is used
with a limited set of blurred RGB images to refine the appearance. Extensive
experiments on multiple datasets validate the superior rendering quality of
Event3DGS compared with existing approaches, with over 95% lower training time
and faster rendering speed in orders of magnitude.

通过利用事件相机的高时间分辨率和显式的基于点的表示，Event3DGS 可以在快速自我运动下仅根据事件流重建高保真度的 3D 结构，并通过稀疏感知的采样和渐进训练方法获得更好的重建质量和一致性，同时通过将运动模糊形成过程显式地结合到可微分的光栅化器中，通过有限的一组模糊的 RGB 图像来改善外观。大量实验证明，与现有方法相比，Event3DGS 具有卓越的渲染质量，并且训练时间减少了 95％以上，并且在渲染速度上具有数量级的提升。