The field of novel-view synthesis has recently witnessed the emergence of 3D Gaussian Splatting, which represents scenes in a point-based manner and renders through rasterization. This methodology, in contrast to Radiance Fields that rely on ray tracing, demonstrates superior rendering quality and speed. However, the explicit and unstructured nature of 3D Gaussians poses a significant storage challenge, impeding its broader application. To address this challenge, we introduce the Gaussian-Forest modeling framework, which hierarchically represents a scene as a forest of hybrid 3D Gaussians. Each hybrid Gaussian retains its unique explicit attributes while sharing implicit ones with its sibling Gaussians, thus optimizing parameterization with significantly fewer variables. Moreover, adaptive growth and pruning strategies are designed, ensuring detailed representation in complex regions and a notable reduction in the number of required Gaussians. Extensive experiments demonstrate that Gaussian-Forest not only maintains comparable speed and quality but also achieves a compression rate surpassing 10 times, marking a significant advancement in efficient scene modeling. Codes are available at https://github.com/Xian-Bei/GaussianForest.

翻译：新视角合成领域近期涌现出三维高斯泼溅技术，该技术以点云形式表征场景并通过光栅化实现渲染。与依赖光线追踪的辐射场方法相比，该方法展现出更优的渲染质量与速度。然而，三维高斯的显式与非结构化特性带来了显著的存储挑战，制约其广泛应用。针对该问题，我们提出高斯森林建模框架，通过层级化方式将场景表征为混合三维高斯的森林结构。每个混合高斯在保留独特显式属性的同时，与同组高斯共享隐式属性，从而以显著更少的变量实现参数优化。此外，我们设计了自适应生长与剪枝策略，确保复杂区域的细节表征能力，同时大幅减少所需高斯数量。大量实验表明，高斯森林不仅保持可比拟的渲染速度与质量，更实现了超10倍的压缩率，标志着高效场景建模的重要突破。代码已开源至 https://github.com/Xian-Bei/GaussianForest。