Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation. However, the substantial data volume of Gaussian splatting impedes its practical utility in real-world applications. Herein, we propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS), which harnesses compact Gaussian primitives for faithful 3D scene modeling with a remarkably reduced data size. To ensure the compactness of Gaussian primitives, we devise a hybrid primitive structure that captures predictive relationships between each other. Then, we exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms. Moreover, we develop a rate-constrained optimization scheme to eliminate redundancies within such hybrid primitives, steering our CompGS towards an optimal trade-off between bitrate consumption and representation efficacy. Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality. Our code will be released on GitHub for further research.

翻译：高斯溅射凭借其卓越的渲染质量和效率，已成为三维场景表示领域的重要技术。然而，高斯溅射模型庞大的数据量限制了其在实际应用中的可行性。为此，我们提出了一种名为压缩高斯溅射（CompGS）的高效三维场景表示方法，该方法利用紧凑的高斯基元实现精准的三维场景建模，同时显著缩减数据规模。为确保高斯基元的紧凑性，我们设计了一种混合基元结构，用以捕获基元间的预测关联。随后，通过少量锚定基元进行预测，使大部分基元可被封装为高度紧凑的残差形式。此外，我们提出了一种率约束优化方案，以消除混合基元中的冗余信息，引导CompGS在比特率消耗与表示效能之间达到最优平衡。实验结果表明，所提出的CompGS显著优于现有方法，在保证模型精度与渲染质量的前提下，实现了三维场景表示的高度紧凑性。相关代码将在GitHub上开源以供进一步研究。