Reconstructing sharp 3D representations from blurry multi-view images are long-standing problem in computer vision. Recent works attempt to enhance high-quality novel view synthesis from the motion blur by leveraging event-based cameras, benefiting from high dynamic range and microsecond temporal resolution. However, they often reach sub-optimal visual quality in either restoring inaccurate color or losing fine-grained details. In this paper, we present DiET-GS, a diffusion prior and event stream-assisted motion deblurring 3DGS. Our framework effectively leverages both blur-free event streams and diffusion prior in a two-stage training strategy. Specifically, we introduce the novel framework to constraint 3DGS with event double integral, achieving both accurate color and well-defined details. Additionally, we propose a simple technique to leverage diffusion prior to further enhance the edge details. Qualitative and quantitative results on both synthetic and real-world data demonstrate that our DiET-GS is capable of producing significantly better quality of novel views compared to the existing baselines. Our project page is https://diet-gs.github.io

翻译：从模糊的多视角图像重建清晰的3D表示是计算机视觉中长期存在的问题。近期研究尝试利用基于事件的相机（受益于其高动态范围和微秒级时间分辨率）来增强从运动模糊中合成高质量新视角的能力。然而，这些方法通常在恢复不准确的颜色或丢失细粒度细节方面达到次优的视觉质量。本文提出DiET-GS，一种基于扩散先验与事件流辅助的运动去模糊3D高斯溅射方法。我们的框架通过两阶段训练策略，有效结合了无模糊事件流和扩散先验。具体而言，我们引入了一种新颖的框架，通过事件二重积分对3DGS进行约束，从而同时实现准确的颜色还原和清晰的细节保留。此外，我们提出一种简单技术，利用扩散先验进一步强化边缘细节。在合成数据与真实数据上的定性与定量结果表明，相较于现有基线方法，我们的DiET-GS能够生成质量显著更优的新视角图像。项目页面为 https://diet-gs.github.io