Although significant progress has been made in reconstructing sharp 3D scenes from motion-blurred images, a transition to real-world applications remains challenging. The primary obstacle stems from the severe blur which leads to inaccuracies in the acquisition of initial camera poses through Structure-from-Motion, a critical aspect often overlooked by previous approaches. To address this challenge, we propose DeblurGS, a method to optimize sharp 3D Gaussian Splatting from motion-blurred images, even with the noisy camera pose initialization. We restore a fine-grained sharp scene by leveraging the remarkable reconstruction capability of 3D Gaussian Splatting. Our approach estimates the 6-Degree-of-Freedom camera motion for each blurry observation and synthesizes corresponding blurry renderings for the optimization process. Furthermore, we propose Gaussian Densification Annealing strategy to prevent the generation of inaccurate Gaussians at erroneous locations during the early training stages when camera motion is still imprecise. Comprehensive experiments demonstrate that our DeblurGS achieves state-of-the-art performance in deblurring and novel view synthesis for real-world and synthetic benchmark datasets, as well as field-captured blurry smartphone videos.

翻译：尽管从运动模糊图像中重建清晰三维场景已取得显著进展，但其向实际应用的过渡仍面临挑战。主要障碍源于严重模糊导致通过运动恢复结构获取初始相机位姿时产生误差，而这一关键问题在以往方法中常被忽视。为此，我们提出DeblurGS方法，能够在存在噪声相机位姿初始化的条件下，从运动模糊图像中优化出清晰的3D高斯泼溅模型。我们借助3D高斯泼溅卓越的重建能力还原细粒度清晰场景，通过估计每次模糊观测对应的六自由度相机运动轨迹，在优化过程中合成相应的模糊渲染结果。此外，我们提出高斯致密化退火策略，避免在初始训练阶段（相机运动仍不精确时）在错误位置生成不准确的高斯体。综合实验表明，DeblurGS在真实场景与合成基准数据集以及现场拍摄的模糊智能手机视频中，均达到了去模糊与新颖视角合成任务的最新性能水平。