CompGS: Efficient 3D Scene Representation via Compressed Gaussian
  Splatting
Xiangrui Liu Xinju Wu Pingping Zhang Shiqi Wang Zhu Li Sam Kwong
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.