We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes. To this end, we first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effectively encodes locally-coherent Gaussian attributes using a neural field representation with a minimal storage requirement. On top of the novel representation, LocoGS is carefully designed with additional components such as dense initialization, an adaptive spherical harmonics bandwidth scheme and different encoding schemes for different Gaussian attributes to maximize compression performance. Experimental results demonstrate that our approach outperforms the rendering quality of existing compact Gaussian representations for representative real-world 3D datasets while achieving from 54.6× to 96.6× compressed storage size and from 2.1× to 2.4× rendering speed than 3DGS. Even our approach also demonstrates an averaged 2.4× higher rendering speed than the state-of-the-art compression method with comparable compression performance.
我们提出了 LocoGS，一种面向局部性的 3D 高斯点绘制（3D Gaussian Splatting, 3DGS）框架，通过利用 3D 高斯的空间一致性，实现体积场景的紧凑建模。为此，我们首先分析了 3D 高斯属性的局部一致性，并提出了一种新颖的面向局部性的 3D 高斯表示法。该方法利用神经场表示高效编码局部一致的高斯属性，同时最大限度地减少存储需求。 基于这一新颖表示，LocoGS 经过精心设计，加入了若干附加组件，例如稠密初始化、自适应球谐带宽方案，以及针对不同高斯属性的差异化编码策略，以最大化压缩性能。实验结果表明，我们的方法在代表性的真实 3D 数据集上，不仅在渲染质量上优于现有的紧凑高斯表示，还实现了 54.6 倍至 96.6 倍 的存储压缩比，并在渲染速度上比传统 3DGS 快 2.1 倍至 2.4 倍。此外，与当前最先进的压缩方法相比，LocoGS 的渲染速度平均提高 2.4 倍，同时保持了可比的压缩性能。 这一结果表明，LocoGS 在存储效率和渲染性能方面达到了新的高度，是一种高效且适应性强的 3D 场景建模方法。