Abstract
The reconstruction of dynamic 3D scenes using 3D Gaussian Splatting has shown significant promise. A key challenge, however, remains in modeling realistic motion, as most methods fail to align the motion of Gaussians with real-world physical dynamics. This misalignment is particularly problematic for monocular video datasets, where failing to maintain coherent motion undermines local geometric structure, ultimately leading to degraded reconstruction quality. Consequently, many state-of-the-art approaches rely heavily on external priors, such as optical flow or 2D tracks, to enforce temporal coherence. In this work, we propose a novel method to explicitly preserve the local geometric structure of Gaussians across time in 4D scenes. Our core idea is to introduce a view-space ray grouping strategy that clusters Gaussians intersected by the same ray, considering only those whose $\alpha$-blending weights exceed a threshold. We then apply constraints to these groups to maintain a consistent spatial distribution, effectively preserving their local geometry. This approach enforces a more physically plausible motion model by ensuring that local geometry remains stable over time, eliminating the reliance on external guidance. We demonstrate the efficacy of our method by integrating it into two distinct baseline models. Extensive experiments on challenging monocular datasets show that our approach significantly outperforms existing methods, achieving superior temporal consistency and reconstruction quality.
Abstract (translated)
利用3D高斯泼溅技术重建动态3D场景已展现出显著前景。然而，一个关键挑战仍在于模拟逼真的运动——大多数方法未能使高斯元的运动与真实物理动力学保持一致。这种偏差在单目视频数据集中尤为突出，因为无法维持连贯的运动会破坏局部几何结构，最终导致重建质量下降。因此，许多先进方法严重依赖光流或二维轨迹等外部先验信息来强制实现时间一致性。本研究提出一种新方法，旨在4D场景中显式保持高斯元跨时间的局部几何结构。核心思路是引入视图空间射线分组策略：将同一射线相交的高斯元进行聚类，仅考虑α混合权重超过阈值的高斯元，随后对这些分组施加约束以维持稳定的空间分布，从而有效保留其局部几何形态。该方法通过确保局部几何随时间保持稳定，构建了更符合物理规律的运动模型，并消除了对外部引导的依赖。我们将该方法集成到两个不同的基线模型中验证其有效性，在具有挑战性的单目数据集上的大量实验表明，本方案显著优于现有方法，在时间一致性和重建质量两方面均达到更优表现。
URL
https://arxiv.org/abs/2603.24994
