EaDeblur-GS：事件辅助的基于高斯泼溅的3D去模糊重建
EaDeblur-GS: Event assisted 3D Deblur Reconstruction with Gaussian Splatting
AI总结：
本文提出EaDeblur-GS方法，通过融合事件相机数据并利用自适应偏差估计器网络估计高斯中心偏差，增强了3D高斯泼溅对运动模糊的鲁棒性，实现了实时清晰的3D去模糊重建。
摘要：
随着神经辐射场（NeRF）和3D高斯泼溅（3DGS）的发展，3D去模糊重建技术近期取得了显著进展。尽管这些技术能够从模糊图像输入中恢复相对清晰的3D重建，但它们在处理严重模糊和复杂相机运动方面仍面临局限。为解决这些问题，我们提出了事件辅助的基于高斯泼溅的3D去模糊重建方法（EaDeblur-GS），该方法融合事件相机数据以增强3DGS对运动模糊的鲁棒性。通过采用自适应偏差估计器（ADE）网络来估计高斯中心偏差，并使用新颖的损失函数，EaDeblur-GS实现了实时的清晰3D重建，展现出与最先进方法相当的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
