通过量化每个高斯点的置信度，提升单目动态三维重建的稳定性与视角泛化能力。
Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
- 基于时空图建模高斯点置信度，区分可靠与不可靠观测。
- 在真实与合成数据上均减少遮挡下的几何漂移，提升极端视角生成质量。
- 适合需要高鲁棒性动态场景重建的研究者或工业应用。
从单目输入重建动态3D场景本质上是欠约束的，遮挡和极端新视图会引入不确定性。尽管动态高斯点阵列提供了高效表示，但传统模型对所有高斯点进行均匀优化，忽视了其可观测性差异。这导致遮挡时出现运动漂移，且在未见视点下合成质量下降。本文提出：可观测性高的高斯点可作为可靠的运动锚点，而可见性差的则应被低权重对待。为此，我们提出USplat4D，一种新型不确定性感知的动态高斯点阵列框架，通过传播可靠运动线索提升4D重建效果。该方法估计随时间变化的每高斯点不确定性，并构建时空图实现不确定性感知优化。在多样真实与合成数据集上的实验表明，显式建模不确定性显著提升动态高斯点阵列性能，在遮挡下保持更稳定几何结构，极端视点下生成质量更高。
原文摘要 · Abstract (English)
Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating to unseen views. We argue that uncertainty matters: Gaussians with recurring observations across views and time act as reliable anchors to guide motion, whereas those with limited visibility are treated as less reliable. To this end, we introduce USplat4D, a novel Uncertainty-aware dynamic Gaussian Splatting framework that propagates reliable motion cues to enhance 4D reconstruction. Our approach estimates time-varying per-Gaussian uncertainty and leverages it to construct a spatio-temporal graph for uncertainty-aware optimization. Experiments on diverse real and synthetic datasets show that explicitly modeling uncertainty consistently improves dynamic Gaussian Splatting models, yielding more stable geometry under occlusion and high-quality synthesis at extreme viewpoints.
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