MotionGS：探索用于可变形3D高斯泼溅的显式运动指导
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
AI总结：
提出MotionGS框架，通过光流解耦模块提取运动流约束3D高斯形变，并结合相机位姿优化模块，在单目动态场景重建中实现定性与定量的最优性能。
摘要：
动态场景重建是3D视觉领域的一项长期挑战。近期，3D Gaussian Splatting的出现为这一问题提供了新的见解。尽管后续工作迅速将静态3D高斯扩展至动态场景，但它们通常缺乏对物体运动的显式约束，导致优化困难和性能下降。为解决上述问题，我们提出了一种名为MotionGS的新型可变形3D高斯泼溅框架，该框架探索显式运动先验以指导3D高斯的形变。具体而言，我们首先引入一个光流解耦模块，将光流解耦为相机流和运动流，分别对应于相机运动和物体运动。随后，运动流能够有效约束3D高斯的形变，从而模拟动态物体的运动。此外，还提出了一个相机位姿优化模块，用于交替优化3D高斯和相机位姿，以减轻不准确相机位姿的影响。在单目动态场景中的大量实验验证了MotionGS超越了现有最先进的方法，并在定性和定量结果上均展现出显著的优越性。项目页面：https://ruijiezhu94.github.io/MotionGS_page
英文摘要：
Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic scenes, they often lack explicit constraints on object motion, leading to optimization difficulties and performance degradation. To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the deformation of 3D Gaussians. Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively. Then the motion flow can effectively constrain the deformation of 3D Gaussians, thus simulating the motion of dynamic objects. Additionally, a camera pose refinement module is proposed to alternately optimize 3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses. Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods and exhibits significant superiority in both qualitative and quantitative results. Project page: https://ruijiezhu94.github.io/MotionGS_page
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。
