  3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in 3D
scene reconstruction and novel view synthesis. However, its training heavily
depends on high-quality, sharp images and accurate camera poses. Fulfilling
these requirements can be challenging in non-ideal real-world scenarios, where
motion-blurred images are commonly encountered in high-speed moving cameras or
low-light environments that require long exposure times. To address these
challenges, we introduce Event Stream Assisted Gaussian Splatting
(EvaGaussians), a novel approach that integrates event streams captured by an
event camera to assist in reconstructing high-quality 3D-GS from blurry images.
Capitalizing on the high temporal resolution and dynamic range offered by the
event camera, we leverage the event streams to explicitly model the formation
process of motion-blurred images and guide the deblurring reconstruction of
3D-GS. By jointly optimizing the 3D-GS parameters and recovering camera motion
trajectories during the exposure time, our method can robustly facilitate the
acquisition of high-fidelity novel views with intricate texture details. We
comprehensively evaluated our method and compared it with previous
state-of-the-art deblurring rendering methods. Both qualitative and
quantitative comparisons demonstrate that our method surpasses existing
techniques in restoring fine details from blurry images and producing
high-fidelity novel views.
