3D deblurring reconstruction techniques have recently seen significant advancements with the development of Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Although these techniques can recover relatively clear 3D reconstructions from blurry image inputs, they still face limitations in handling severe blurring and complex camera motion. To address these issues, we propose Event-assisted 3D Deblur Reconstruction with Gaussian Splatting (EaDeblur-GS), which integrates event camera data to enhance the robustness of 3DGS against motion blur. By employing an Adaptive Deviation Estimator (ADE) network to estimate Gaussian center deviations and using novel loss functions, EaDeblur-GS achieves sharp 3D reconstructions in real-time, demonstrating performance comparable to state-of-the-art methods.

3D去模糊重建技术的研究在最近得到了显著的进展，Neural Radiance Fields (NeRF)和3D Gaussian Splatting (3DGS)的发展使得它们能够从模糊的图像输入中恢复相对清晰的3D重建，但仍然存在对严重模糊和复杂摄像机运动的处理限制。为了解决这些问题，我们提出了一种基于事件辅助的3D去模糊重建和高斯扰动(EaDeblur-GS)的方法，该方法将事件相机数据集成到3DGS中，增强其对运动模糊的鲁棒性。通过使用自适应偏差估计器(Adaptive Deviation Estimator，ADE)网络来估计高斯中心偏差，并使用新颖的损失函数，EaDeblur-GS能够实时实现清晰的3D重建，表现与最先进的方法相当。

EaDeblur-GS: 基于事件辅助的高斯重构的三维去模糊