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.
3D 高斯点光源（3DGS）最近作为一种替代表示方法出现，利用基于 3D 高斯点的表示并引入了近似的体积渲染，实现了非常快的渲染速度和有希望的图像质量。此外，后续研究成功地将 3DGS 扩展到了动态 3D 场景，展示了其广泛的应用。然而，一个显著的缺点是 3DGS 及其后续方法需要大量的高斯点来保持渲染图像的高保真度，这需要大量的内存和存储。为了解决这个关键问题，我们特别关注两个主要目标：在不牺牲性能的情况下减少高斯点的数量，以及压缩高斯属性（如视角依赖的颜色和协方差）。为此，我们提出了一种可学习的掩码策略，显著减少了高斯点的数量，同时保持高性能。此外，我们通过使用基于网格的神经场而不是依赖球面谐波，提出了一种紧凑但有效的视角依赖颜色表示方法。最后，我们通过残差向量量化学习代码簿，以紧凑地表示几何和时间属性。通过量化和熵编码等模型压缩技术，我们在静态场景中相比于 3DGS 实现了超过 25 倍的存储减少和增强的渲染速度，同时保持了场景表示的质量。在动态场景中，我们的方法相比于现有的最先进方法实现了超过 12 倍的存储效率，并保持了高质量的重建。我们的工作提供了一个全面的 3D 场景表示框架，实现了高性能、快速训练、紧凑性和实时渲染。