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 achieve a compact size, we propose HAC++, which leverages the relationships between unorganized anchors and a structured hash grid, utilizing their mutual information for context modeling. Additionally, HAC++ captures intra-anchor contextual relationships to further enhance compression performance. To facilitate entropy coding, we utilize Gaussian distributions to precisely 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. Moreover, we incorporate an adaptive masking strategy to eliminate invalid Gaussians and anchors. Overall, HAC++ achieves a remarkable size reduction of over 100X compared to vanilla 3DGS when averaged on all datasets, while simultaneously improving fidelity. It also delivers more than 20X size reduction compared to Scaffold-GS.
3D Gaussian Splatting (3DGS) 已成为一种极具前景的新视角合成框架，兼具快速渲染速度和高保真度。然而，大量高斯点及其相关属性对高效压缩技术提出了严峻挑战。此外，高斯点云（或本文中的锚点）稀疏且无序的特性进一步增加了压缩的难度。为实现紧凑的存储尺寸，我们提出了 HAC++，该方法利用无序锚点与结构化哈希网格之间的关系，通过其相互信息进行上下文建模。此外，HAC++ 还捕捉锚点内部的上下文关系，从而进一步提升压缩性能。为支持熵编码，我们利用高斯分布精确估计每个量化属性的概率，并设计了自适应量化模块，以实现这些属性的高精度量化，从而提高保真度的恢复效果。与此同时，我们引入了一种自适应掩蔽策略，用于剔除无效的高斯点和锚点。总体而言，HAC++ 在所有数据集上的平均压缩率较原始 3DGS 实现了超过 100 倍的尺寸缩减，同时提升了保真度。相比 Scaffold-GS，HAC++ 还实现了超过 20 倍的尺寸压缩。