DAS3R：面向静态场景重建的动态感知高斯泼溅
DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction
AI总结：
DAS3R通过动态感知高斯泼溅和运动掩码实现日常视频的静态背景重建，无需相机位姿或SLAM点云，在DAVIS和Sintel上PSNR领先超过2 dB。
摘要：
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。通过整合训练得到的运动掩码，并将静态场景建模为具有动态感知优化的高斯泼溅，我们的方法实现了比以往工作更准确的背景重建结果。我们提出的方法称为DAS3R，即“面向静态场景重建的动态感知高斯泼溅”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显著部分的视频，并且不需要相机位姿输入或来自基于SLAM方法的点云数据。我们在DAVIS和Sintel数据集上将DAS3R与最近的无干扰物方法进行了比较；DAS3R展现出更优的性能和鲁棒性，在PSNR上领先超过2 dB。项目网页可通过\url{https://kai422.github.io/DAS3R/}访问。
英文摘要：
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. The project's webpage can be accessed via \url{https://kai422.github.io/DAS3R/}
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。
