  3D Gaussian splatting (3DGS) has recently emerged as an alternative
representation that leverages a 3D Gaussian-based representation and introduces
an approximated volumetric rendering, achieving very fast rendering speed and
promising image quality. Furthermore, subsequent studies have successfully
extended 3DGS to dynamic 3D scenes, demonstrating its wide range of
applications. However, a significant drawback arises as 3DGS and its following
methods entail a substantial number of Gaussians to maintain the high fidelity
of the rendered images, which requires a large amount of memory and storage. To
address this critical issue, we place a specific emphasis on two key
objectives: reducing the number of Gaussian points without sacrificing
performance and compressing the Gaussian attributes, such as view-dependent
color and covariance. To this end, we propose a learnable mask strategy that
significantly reduces the number of Gaussians while preserving high
performance. In addition, we propose a compact but effective representation of
view-dependent color by employing a grid-based neural field rather than relying
on spherical harmonics. Finally, we learn codebooks to compactly represent the
geometric and temporal attributes by residual vector quantization. With model
compression techniques such as quantization and entropy coding, we consistently
show over 25x reduced storage and enhanced rendering speed compared to 3DGS for
static scenes, while maintaining the quality of the scene representation. For
dynamic scenes, our approach achieves more than 12x storage efficiency and
retains a high-quality reconstruction compared to the existing state-of-the-art
methods. Our work provides a comprehensive framework for 3D scene
representation, achieving high performance, fast training, compactness, and
real-time rendering. Our project page is available at
https://maincold2.github.io/c3dgs/.
