Our work is the pioneer to explore context-based compression for 3DGS representation, resulting in a remarkable size reduction of over 75X compared to vanilla 3DGS, while simultaneously improving fidelity, and achieving over 11X size reduction over SOTA 3DGS compression approach Scaffold-GS.
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.
Overview of our HAC framework. It is based on Scaffold-GS (top), which introduces anchors with their attributes to neural-predict 3D Gaussian attributes. Middle: Our HAC framework jointly learns structured compact hash grid (binarized for each parameter) that can be queried at any anchor location to obtain the interpolated hash feature and use it for context modeling of anchor attributes. Please refer to the paper for more details.
@article{hac2024,
author = {Chen, Yihang and Wu, Qianyi and Cai, Jianfei and Harandi, Mehrtash and Lin, Weiyao},
title = {HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression},
journal = {arXiv preprint arXiv:2403.14530},
year = {2024},
}