We propose a novel framework for scene decomposition and static background reconstruction from everyday videos. By integrating the trained motion masks and modeling the static scene as Gaussian splats with dynamics-aware optimization, our method achieves more accurate background reconstruction results than previous works. Our proposed method is termed DAS3R, an abbreviation for Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction. Compared to existing methods, DAS3R is more robust in complex motion scenarios, capable of handling videos where dynamic objects occupy a significant portion of the scene, and does not require camera pose inputs or point cloud data from SLAM-based methods. We compared DAS3R against recent distractor-free approaches on the DAVIS and Sintel datasets; DAS3R demonstrates enhanced performance and robustness with a margin of more than 2 dB in PSNR.
我们提出了一种新颖的框架，用于从日常视频中进行场景分解和静态背景重建。通过集成训练好的运动掩码，并将静态场景建模为具有动态感知优化的高斯喷射，我们的方法比现有工作实现了更准确的背景重建结果。我们将该方法命名为 DAS3R，即 Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction 的缩写。 与现有方法相比，DAS3R 在复杂运动场景中更加鲁棒，能够处理动态物体占据场景大部分的视频，并且无需相机位姿输入或基于 SLAM 方法的点云数据。在 DAVIS 和 Sintel 数据集上，我们将 DAS3R 与最近的无干扰方法进行了比较；结果表明，DAS3R 在性能和鲁棒性上均有显著提升，PSNR 指标提高了 2 dB 以上。