提出高效压缩3D高斯点云的熵编码方法，显著降低存储传输开销。
EntropyGS: An Efficient Entropy Coding on 3D Gaussian Splatting
- 基于属性分布特性设计分治熵编码，按类型自适应量化
- 在基准数据集上实现约30倍率减少，渲染质量基本不变
- 适合需要压缩3D高斯点云的应用场景，如实时渲染与传输
作为一种新兴的视图合成方法，3D高斯点云（3DGS）展现出快速训练与渲染及优异视觉质量。3DGS的高斯生成与视图渲染通常在时间或设备上分离，因此对3DGS高斯的存储、传输与压缩成为必要。本文对3DGS高斯属性进行相关性与统计分析，发现球谐系数AC属性近似服从拉普拉斯分布，而旋转、缩放与不透明度可由高斯混合分布逼近。此外，谐波系数属性与其他属性相关性较弱，仅继承颜色空间的关联性。为此，提出一种参数化分治熵编码方法EntropyGS：编码时估计各高斯属性的分布参数以辅助熵编码，根据属性类型自适应执行量化。EntropyGS在基准数据集上实现约30倍码率压缩，同时保持与原始3DGS数据相当的渲染质量，且编码解码速度快。
原文摘要 · Abstract (English)
As an emerging novel view synthesis approach, 3D Gaussian Splatting (3DGS) demonstrates fast training/rendering with superior visual quality. The two tasks of 3DGS, Gaussian creation and view rendering, are typically separated over time or devices, and thus storage/transmission and finally compression of 3DGS Gaussians become necessary. We begin with a correlation and statistical analysis of 3DGS Gaussian attributes. An inspiring finding in this work reveals that spherical harmonic AC attributes precisely follow Laplace distributions, while mixtures of Gaussian distributions can approximate rotation, scaling, and opacity. Additionally, harmonic AC attributes manifest weak correlations with other attributes except for inherited correlations from a color space. A factorized and parameterized entropy coding method, EntropyGS, is hereinafter proposed. During encoding, distribution parameters of each Gaussian attribute are estimated to assist their entropy coding. The quantization for entropy coding is adaptively performed according to Gaussian attribute types. EntropyGS demonstrates about 30x rate reduction on benchmark datasets while maintaining similar rendering quality compared to input 3DGS data, with a fast encoding and decoding time.
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