UPGS：面向动态场景模糊的统一姿态感知高斯点分布
UPGS: Unified Pose-aware Gaussian Splatting for Dynamic Scene Deblurring
AI总结：
本文提出统一优化框架，通过将相机姿态作为可学习参数与3DGS属性结合，实现端到端优化，提升动态去模糊的重建质量和姿态估计精度。
摘要：
从单目视频重建动态3D场景在AR/VR、机器人和自主导航中有广泛应用，但常因相机和物体运动导致的严重运动模糊而失败。现有方法通常采用两步流程，先估计相机姿态再优化3D高斯体。由于模糊伪影通常会破坏姿态估计，姿态误差可能累积导致重建结果较差。为此，我们引入统一优化框架，将相机和物体运动作为对3D高斯体的每 primitives SE(3)仿射变换，并制定统一优化目标。为保证优化稳定性，我们引入三阶段训练计划，交替优化相机姿态和高斯体。特别地，首先训练3D高斯体，姿态固定，然后优化姿态，高斯体保持不变。最后，所有可学习参数一起优化。在Stereo Blur数据集和具有挑战性的现实序列上进行的大量实验表明，我们的方法在重建质量和姿态估计精度上显著优于先前的动态去模糊方法。
英文摘要：
Reconstructing dynamic 3D scenes from monocular video has broad applications in AR/VR, robotics, and autonomous navigation, but often fails due to severe motion blur caused by camera and object motion. Existing methods commonly follow a two-step pipeline, where camera poses are first estimated and then 3D Gaussians are optimized. Since blurring artifacts usually undermine pose estimation, pose errors could be accumulated to produce inferior reconstruction results. To address this issue, we introduce a unified optimization framework by incorporating camera poses as learnable parameters complementary to 3DGS attributes for end-to-end optimization. Specifically, we recast camera and object motion as per-primitive SE(3) affine transformations on 3D Gaussians and formulate a unified optimization objective. For stable optimization, we introduce a three-stage training schedule that optimizes camera poses and Gaussians alternatively. Particularly, 3D Gaussians are first trained with poses being fixed, and then poses are optimized with 3D Gaussians being untouched. Finally, all learnable parameters are optimized together. Extensive experiments on the Stereo Blur dataset and challenging real-world sequences demonstrate that our method achieves significant gains in reconstruction quality and pose estimation accuracy over prior dynamic deblurring 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。
