MoBGS: 用于模糊单目视频的运动去模糊动态3D高斯点云技术
MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video
AI总结：
MoBGS通过模糊适应性潜在相机估计和曝光估计方法，实现对模糊单目视频中动态场景的高效去模糊和高质量时空视图重建。
摘要：
我们提出了MoBGS，一种新颖的运动去模糊3D高斯点云（3DGS）框架，能够通过端到端的方式从模糊单目视频中重建清晰且高质量的时空视图。现有动态新视图合成（NVS）方法对随意拍摄视频中的运动模糊高度敏感，导致渲染质量显著下降。尽管近期方法针对运动模糊输入进行NVS处理，但它们主要集中在静态场景重建，缺乏对动态物体的专门运动建模。为克服这些限制，我们的MoBGS引入了一种新颖的模糊适应性潜在相机估计（BLCE）方法，使用所提出的模糊适应性神经常微分方程（ODE）求解器进行有效的潜在相机轨迹估计，提升全局相机运动去模糊。此外，我们提出了一种潜在相机诱导曝光估计（LCEE）方法，以确保全局相机和局部物体运动的去模糊一致性。在Stereo Blur数据集和真实世界模糊视频上的广泛实验表明，我们的MoBGS显著优于最近的方法，实现了在运动模糊下的动态NVS状态最先进性能。
英文摘要：
We present MoBGS, a novel motion deblurring 3D Gaussian Splatting (3DGS) framework capable of reconstructing sharp and high-quality novel spatio-temporal views from blurry monocular videos in an end-to-end manner. Existing dynamic novel view synthesis (NVS) methods are highly sensitive to motion blur in casually captured videos, resulting in significant degradation of rendering quality. While recent approaches address motion-blurred inputs for NVS, they primarily focus on static scene reconstruction and lack dedicated motion modeling for dynamic objects. To overcome these limitations, our MoBGS introduces a novel Blur-adaptive Latent Camera Estimation (BLCE) method using a proposed Blur-adaptive Neural Ordinary Differential Equation (ODE) solver for effective latent camera trajectory estimation, improving global camera motion deblurring. In addition, we propose a Latent Camera-induced Exposure Estimation (LCEE) method to ensure consistent deblurring of both a global camera and local object motions. Extensive experiments on the Stereo Blur dataset and real-world blurry videos show that our MoBGS significantly outperforms the very recent methods, achieving state-of-the-art performance for dynamic NVS under motion blur.
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