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.

提出了一种名为 CompGS 的高效的三维场景表示方法，通过使用紧凑的高斯原始形式，实现了对三维场景建模过程中数据大小的显著降低。通过设计一种捕获彼此之间预测关系的混合原始结构，利用少量的锚原始形式进行预测，并将大多数原始形式封装成高度紧凑的残差形式。此外，还开发了一种受速率约束的优化方案，以在比特率消耗和表示效能之间实现 CompGS 的最佳权衡。实验证明，所提出的 CompGS 在三维场景表示中明显优于现有方法，在不损害模型准确性和渲染质量的情况下实现了卓越的紧凑性。我们的代码将在 GitHub 上发布供进一步研究使用。