分离运动与几何，让动态场景重建更清晰
Decoupling Motion and Geometry in 4D Gaussian Splatting
- 用速度驱动的剪切矩阵解耦运动与几何
- 在公开数据集上达到当前最佳重建效果
- 适合做动态3D场景建模的研究者参考
高保真动态场景重建是重要但具有挑战性的问题。尽管近期的4D高斯点阵（4DGS）已能建模时间动态，但其将高斯运动与几何属性耦合在单一协方差中，限制了对复杂运动的表达能力，并常导致视觉伪影。为此，我们提出VeGaS——一种基于速度的新型4D高斯点阵框架，实现高斯运动与几何的解耦。具体地，引入伽利略剪切矩阵，显式融入随时间变化的速度以灵活建模复杂非线性运动，同时严格隔离运动对几何相关条件协方差的影响。此外，设计几何形变网络，利用时空上下文和速度线索优化高斯形状与方向，增强时序几何建模能力。在多个公开数据集上的大量实验表明，VeGaS性能达到当前最优。
原文摘要 · Abstract (English)
High-fidelity reconstruction of dynamic scenes is an important yet challenging problem. While recent 4D Gaussian Splatting (4DGS) has demonstrated the ability to model temporal dynamics, it couples Gaussian motion and geometric attributes within a single covariance formulation, which limits its expressiveness for complex motions and often leads to visual artifacts. To address this, we propose VeGaS, a novel velocity-based 4D Gaussian Splatting framework that decouples Gaussian motion and geometry. Specifically, we introduce a Galilean shearing matrix that explicitly incorporates time-varying velocity to flexibly model complex non-linear motions, while strictly isolating the effects of Gaussian motion from the geometry-related conditional Gaussian covariance. Furthermore, a Geometric Deformation Network is introduced to refine Gaussian shapes and orientations using spatio-temporal context and velocity cues, enhancing temporal geometric modeling. Extensive experiments on public datasets demonstrate that VeGaS achieves state-of-the-art performance.
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