Existing 4D Gaussian methods for dynamic scene reconstruction offer high visual fidelity and fast rendering. However, these methods suffer from excessive memory and storage demands, which limits their practical deployment. This paper proposes a 4D anchor-based framework that retains visual quality and rendering speed of 4D Gaussians while significantly reducing storage costs. Our method extends 3D scaffolding to 4D space, and leverages sparse 4D grid-aligned anchors with compressed feature vectors. Each anchor models a set of neural 4D Gaussians, each of which represent a local spatiotemporal region. In addition, we introduce a temporal coverage-aware anchor growing strategy to effectively assign additional anchors to under-reconstructed dynamic regions. Our method adjusts the accumulated gradients based on Gaussians' temporal coverage, improving reconstruction quality in dynamic regions. To reduce the number of anchors, we further present enhanced formulations of neural 4D Gaussians. These include the neural velocity, and the temporal opacity derived from a generalized Gaussian distribution. Experimental results demonstrate that our method achieves state-of-the-art visual quality and 97.8% storage reduction over 4DGS.
现有的 4D 高斯方法在动态场景重建中能够提供高视觉保真度和快速渲染，但存在内存和存储需求过高的问题，限制了其实际部署。本文提出了一种基于 4D 锚点的框架，在保留 4D 高斯方法视觉质量和渲染速度的同时，显著降低存储成本。 我们的方法将 3D 框架扩展到 4D 空间，并利用稀疏的 4D 网格对齐锚点和压缩特征向量。每个锚点建模了一组神经 4D 高斯，这些高斯分别表示局部时空区域。此外，我们引入了一个 时间覆盖感知的锚点增长策略，通过为未充分重建的动态区域分配额外的锚点来提高重建质量。我们的方法基于高斯的时间覆盖调整累积梯度，从而提升动态区域的重建效果。 为减少锚点数量，我们进一步提出了增强版神经 4D 高斯的公式，包括神经速度和从广义高斯分布中导出的时间不透明度。实验结果表明，我们的方法在保持最先进视觉质量的同时，将存储需求降低了 97.8%，相较于 4DGS 实现了显著改进。