不确定性在单目4D重建中的动态高斯点云中至关重要
Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
AI总结：
USplat4D通过建模高斯点云的不确定性，提升单目4D重建的稳定性和合成质量。
摘要：
从单目输入中重建动态3D场景本质上是欠约束的，其模糊性源于遮挡和极端新视角。虽然动态高斯点云提供了高效的表示方法，但传统模型对所有高斯点云进行统一优化，忽略了它们是否被良好或劣质观测。这种限制导致在遮挡下出现运动漂移，并在扩展到未见视角时合成质量下降。我们主张不确定性至关重要：在不同视角和时间中反复出现观测的高斯点云充当可靠的锚点以引导运动，而那些可见性有限的则被视为不那么可靠。为此，我们引入了USplat4D，一种新的不确定性感知的动态高斯点云框架，该框架传播可靠的运动提示以增强4D重建。我们的方法估计时间变化的每个高斯点云不确定性，并利用它构建时空图以进行不确定性感知的优化。在多样化的现实和合成数据集上的实验表明，显式建模不确定性一致地提高了动态高斯点云模型，从而在遮挡下获得更稳定的几何结构，并在极端视角下获得高质量的合成效果。
英文摘要：
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.
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。
