用哈希网格压缩3D高斯点云，实现100倍体积缩减。
HAC++: Towards 100X Compression of 3D Gaussian Splatting
- 通过哈希网格建模无序点云的上下文关系，提升压缩效率。
- 平均压缩比超100倍，且图像保真度反而提升。
- 适合需要极致压缩的3D内容部署场景。
3D高斯泼溅（3DGS）在新视角合成中表现出色，渲染快速且保真度高，但其庞大的高斯点及其属性需要高效压缩。然而，高斯点云稀疏且无序，给压缩带来挑战。为此，我们提出HAC++，利用无序点云与结构化哈希网格之间的相互信息进行上下文建模，并捕捉单个高斯点内部的上下文关系以进一步提升压缩性能。为支持熵编码，采用高斯分布精确估计每个量化属性的概率，设计自适应量化模块以实现高精度量化，从而更好恢复保真度。同时引入自适应掩码策略消除无效高斯点和锚点。总体而言，HAC++在所有数据集上平均压缩比超过100倍，相比原始3DGS，同时提升了保真度；相较于Scaffold-GS，压缩比也超过20倍。代码已开源。
原文摘要 · Abstract (English)
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. Our code is available at https://github.com/YihangChen-ee/HAC-plus.
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