3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial Gaussians and their associated attributes necessitate effective compression techniques. Nevertheless, the sparse and unorganized nature of the point cloud of Gaussians (or anchors in our paper) presents challenges for compression. To address this, we make use of the relations between the unorganized anchors and the structured hash grid, leveraging their mutual information for context modeling, and propose a Hash-grid Assisted Context (HAC) framework for highly compact 3DGS representation. Our approach introduces a binary hash grid to establish continuous spatial consistencies, allowing us to unveil the inherent spatial relations of anchors through a carefully designed context model. To facilitate entropy coding, we utilize Gaussian distributions to accurately estimate the probability of each quantized attribute, where an adaptive quantization module is proposed to enable high-precision quantization of these attributes for improved fidelity restoration. Additionally, we incorporate an adaptive masking strategy to eliminate invalid Gaussians and anchors. Importantly, our work is the pioneer to explore context-based compression for 3DGS representation, resulting in a remarkable size reduction of over 75× compared to vanilla 3DGS, while simultaneously improving fidelity, and achieving over 11× size reduction over SOTA 3DGS compression approach Scaffold-GS.
3D高斯喷溅（3DGS）已成为新颖视图合成的一个有前途的框架，以其快速渲染速度和高保真度而自豪。然而，大量的高斯及其相关属性需要有效的压缩技术。尽管如此，高斯点云（或在我们的论文中称为锚点）的稀疏和无组织性质给压缩带来了挑战。为了解决这一问题，我们利用了无组织锚点与结构化哈希网格之间的关系，利用它们的互信息进行上下文建模，并提出了一个哈希网格辅助上下文（HAC）框架，用于高度紧凑的3DGS表示。我们的方法引入了二进制哈希网格以建立连续的空间一致性，允许我们通过精心设计的上下文模型揭示锚点的固有空间关系。为了促进熵编码，我们使用高斯分布来准确估计每个量化属性的概率，其中提出了一个自适应量化模块，以实现这些属性的高精度量化，从而改善保真度恢复。此外，我们加入了一个自适应遮罩策略来消除无效的高斯和锚点。重要的是，我们的工作是首次探索基于上下文的压缩，用于3DGS表示，与普通的3DGS相比，实现了超过75倍的显著大小减少，同时提高了保真度，并且与SOTA 3DGS压缩方法Scaffold-GS相比，实现了超过11倍的大小减少。