  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 $75\times$ compared to vanilla 3DGS, while
simultaneously improving fidelity, and achieving over $11\times$ size reduction
over SOTA 3DGS compression approach Scaffold-GS. Our code is available here:
https://github.com/YihangChen-ee/HAC
