MoBluRF：面向模糊单目视频的运动去模糊神经辐射场
MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video
AI总结：
MoBluRF提出面向模糊单目视频的运动去模糊NeRF框架，通过基础射线初始化和基于运动分解的增量式潜在清晰射线预测，在无掩码监督下实现动态场景的清晰时空视角合成，性能显著超越现有方法。
摘要：
神经辐射场（NeRF）最初为静态场景开发，已启发了许多视频新视角合成技术。然而，视频视角合成面临的挑战来自运动模糊，这是曝光期间物体或相机运动的结果，阻碍了清晰时空视角的精确合成。为此，我们提出了一种新颖的面向模糊单目视频的运动去模糊NeRF框架，称为MoBluRF，由基础射线初始化（BRI）阶段和基于运动分解的去模糊（MDD）阶段组成。在BRI阶段，我们利用给定模糊帧中不准确的相机位姿信息，粗略重建动态3D场景并联合初始化基础射线，这些基础射线进一步用于预测潜在清晰射线。在MDD阶段，我们提出了一种新颖的增量式潜在清晰射线预测（ILSP）方法，通过将潜在清晰射线分解为全局相机运动和局部物体运动分量来处理模糊单目视频帧。我们进一步提出了两个损失函数，用于有效的几何正则化以及静态与动态场景分量的分解，无需任何掩码监督。实验表明，MoBluRF在定性和定量上均以较大幅度优于近期最先进的方法。
英文摘要：
Neural Radiance Fields (NeRF), initially developed for static scenes, have inspired many video novel view synthesis techniques. However, the challenge for video view synthesis arises from motion blur, a consequence of object or camera movements during exposure, which hinders the precise synthesis of sharp spatio-temporal views. In response, we propose a novel motion deblurring NeRF framework for blurry monocular video, called MoBluRF, consisting of a Base Ray Initialization (BRI) stage and a Motion Decomposition-based Deblurring (MDD) stage. In the BRI stage, we coarsely reconstruct dynamic 3D scenes and jointly initialize the base rays which are further used to predict latent sharp rays, using the inaccurate camera pose information from the given blurry frames. In the MDD stage, we introduce a novel Incremental Latent Sharp-rays Prediction (ILSP) approach for the blurry monocular video frames by decomposing the latent sharp rays into global camera motion and local object motion components. We further propose two loss functions for effective geometry regularization and decomposition of static and dynamic scene components without any mask supervision. Experiments show that MoBluRF outperforms qualitatively and quantitatively the recent state-of-the-art methods with large margins.
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。
