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.

翻译：3D高斯泼溅（3DGS）技术的快速发展使得3D高斯模型在三维建模与图像渲染中得到广泛应用，但这也给数据存储与传输带来了巨大挑战。为获得高度紧凑的3DGS表示，我们提出了一种用于高斯泼溅数据压缩的混合熵模型（HEMGS），该模型包含两个核心组件：超先验网络与自回归网络。为有效降低跨属性的结构冗余，我们采用渐进式编码算法生成超先验特征，其中利用已压缩属性与位置信息作为先验。特别地，为更好地从已压缩属性中提取位置特征，我们采用域感知与实例感知架构：前者在无需额外存储开销的情况下捕获域感知的结构关系，后者通过多层感知机揭示场景特异性特征。此外，为降低各属性内部冗余，我们通过自回归网络利用属性内相邻压缩元素间的关联关系。基于其独特结构，我们提出具有灵活感受野的自适应上下文编码算法，以有效捕获相邻压缩元素。整体上，我们将HEMGS集成至端到端优化的3DGS压缩框架中，在四个基准数据集上的大量实验结果表明：在保持渲染质量的前提下，本方法相较于基线方法实现了约40%的平均体积压缩，并达到了当前最优的压缩性能。