统一优化相机位姿与3D高斯，提升动态场景去模糊效果
UPGS: Unified Pose-aware Gaussian Splatting for Dynamic Scene Deblurring
- 将相机与物体运动建模为3D高斯的SE(3)变换，统一优化
- 在Stereo Blur数据集上重建质量提升12.3%，位姿误差降低28%
- 适合需高精度动态3D重建的AR/VR与自动驾驶应用
从单目视频重建动态三维场景在AR/VR、机器人和自动驾驶中有广泛应用，但常因相机与物体运动导致严重运动模糊而失败。现有方法多采用两步流程：先估计相机位姿，再优化3D高斯。由于模糊会干扰位姿估计，误差累积导致重建效果不佳。为此，本文提出一种统一优化框架，将相机位姿作为可学习参数，与3D高斯属性一同进行端到端优化。具体地，将相机与物体运动建模为作用于3D高斯的每原语SE(3)仿射变换，并构建统一优化目标。为确保稳定，设计三阶段训练策略：先固定位姿训练高斯，再固定高斯优化位姿，最后联合优化所有参数。在Stereo Blur数据集及挑战性真实序列上的大量实验表明，本方法在重建质量和位姿估计精度上显著优于已有动态去模糊方法。
原文摘要 · Abstract (English)
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.
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