Fast progress in 3D Gaussian Splatting (3DGS) has made 3D Gaussians popular for 3D modeling and image rendering, but this creates big challenges in data storage and transmission. To obtain a highly compact 3DGS representation, we propose a hybrid entropy model for Gaussian Splatting (HEMGS) data compression, which comprises two primary components, a hyperprior network and an autoregressive network. To effectively reduce structural redundancy across attributes, we apply a progressive coding algorithm to generate hyperprior features, in which we use previously compressed attributes and location as prior information. In particular, to better extract the location features from these compressed attributes, we adopt a domain-aware and instance-aware architecture to respectively capture domain-aware structural relations without additional storage costs and reveal scene-specific features through MLPs. Additionally, to reduce redundancy within each attribute, we leverage relationships between neighboring compressed elements within the attributes through an autoregressive network. Given its unique structure, we propose an adaptive context coding algorithm with flexible receptive fields to effectively capture adjacent compressed elements. Overall, we integrate our HEMGS into an end-to-end optimized 3DGS compression framework and the extensive experimental results on four benchmarks indicate that our method achieves about 40% average reduction in size while maintaining the rendering quality over our baseline method and achieving state-of-the-art compression results.
3D 高斯投影（3D Gaussian Splatting, 3DGS）的快速进展使 3D 高斯在 3D 建模和图像渲染中备受欢迎，但也带来了数据存储和传输方面的巨大挑战。为实现高度紧凑的 3DGS 表示，我们提出了一种用于高斯投影数据压缩的混合熵模型（Hybrid Entropy Model for Gaussian Splatting, HEMGS），该模型由两个主要组件组成：一个超先验网络（hyperprior network）和一个自回归网络（autoregressive network）。 为了有效减少属性间的结构冗余，我们采用了一种渐进编码算法生成超先验特征，利用先前压缩的属性和位置作为先验信息。特别是，为更好地从这些压缩属性中提取位置特征，我们采用了一种域感知和实例感知的架构，通过域感知结构关系捕捉实现无额外存储开销的结构化关系，同时通过多层感知机（MLPs）揭示场景特定特征。 此外，为减少单个属性内的冗余，我们通过自回归网络利用相邻压缩元素之间的关系。基于这一独特结构，我们提出了一种具有灵活感受野的自适应上下文编码算法，有效捕捉相邻压缩元素。 总体而言，我们将 HEMGS 集成到端到端优化的 3DGS 压缩框架中。通过在四个基准数据集上的广泛实验结果表明，与基线方法相比，我们的方法在保持渲染质量的同时，平均将数据大小减少了约 40%，并实现了最先进的压缩效果。