4D Gaussian Splatting (4DGS) has recently emerged as a promising technique for capturing complex dynamic 3D scenes with high fidelity. It utilizes a 4D Gaussian representation and a GPU-friendly rasterizer, enabling rapid rendering speeds. Despite its advantages, 4DGS faces significant challenges, notably the requirement of millions of 4D Gaussians, each with extensive associated attributes, leading to substantial memory and storage cost. This paper introduces a memory-efficient framework for 4DGS. We streamline the color attribute by decomposing it into a per-Gaussian direct color component with only 3 parameters and a shared lightweight alternating current color predictor. This approach eliminates the need for spherical harmonics coefficients, which typically involve up to 144 parameters in classic 4DGS, thereby creating a memory-efficient 4D Gaussian representation. Furthermore, we introduce an entropy-constrained Gaussian deformation technique that uses a deformation field to expand the action range of each Gaussian and integrates an opacity-based entropy loss to limit the number of Gaussians, thus forcing our model to use as few Gaussians as possible to fit a dynamic scene well. With simple half-precision storage and zip compression, our framework achieves a storage reduction by approximately 190× and 125× on the Technicolor and Neural 3D Video datasets, respectively, compared to the original 4DGS. Meanwhile, it maintains comparable rendering speeds and scene representation quality, setting a new standard in the field.
4D高斯散射（4DGS）作为捕捉复杂动态3D场景的高保真技术，最近获得了广泛关注。它利用4D高斯表示和GPU友好的光栅化器，实现了快速渲染速度。尽管具有诸多优势，4DGS仍面临显著挑战，尤其是需要数百万个4D高斯，每个高斯都附带大量属性，导致巨大的内存和存储成本。本文提出了一种内存高效的4DGS框架。我们通过将颜色属性分解为每个高斯的直接颜色分量（仅需3个参数）和一个共享的轻量级交流色彩预测器，从而简化了颜色表示。这一方法消除了传统4DGS中常见的球谐函数系数，后者通常需要多达144个参数，创建了一种内存高效的4D高斯表示。此外，我们引入了一种受限熵的高斯变形技术，该技术使用变形场来扩展每个高斯的作用范围，并结合基于不透明度的熵损失，限制高斯数量，从而迫使模型使用尽可能少的高斯点来很好地拟合动态场景。通过简单的半精度存储和zip压缩，我们的框架在Technicolor和Neural 3D Video数据集上分别实现了约190倍和125倍的存储压缩，相比原始4DGS，在保持相似的渲染速度和场景表示质量的同时，设立了该领域的新标准。