通过显式运动约束提升动态3D高斯点云的重建精度
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
- 分离相机运动与物体运动流，实现精准轨迹建模
- 在单目动态场景中相较现有方法显著提升重建质量
- 适合需要精确动态物体建模的研究者与应用
动态场景重建是3D视觉领域的长期挑战。尽管3D高斯点云的出现为该问题提供了新思路，但现有方法多缺乏对物体运动的显式约束，导致优化困难且性能下降。为此，我们提出MotionGS——一种新型可变形3D高斯点云框架，通过引入显式运动先验来引导3D高斯的形变。具体地，设计光流解耦模块，将光流分解为相机运动流与物体运动流；后者用于有效约束3D高斯的形变，从而模拟动态物体运动。同时，提出相机位姿精修模块，交替优化3D高斯与相机位姿，缓解位姿不准确的影响。在单目动态场景上的大量实验表明，MotionGS优于当前最优方法，在定性与定量结果上均表现卓越。
原文摘要 · Abstract (English)
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
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