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.
近期，新视角合成领域见证了3D高斯喷涂技术的崛起，该技术以点为基础来表示场景，并通过光栅化进行渲染。与依赖光线追踪的辐射场不同，这种方法展示了更优的渲染质量和速度。然而，3D高斯的显式和无结构性质带来了显著的存储挑战，阻碍了其更广泛的应用。为了解决这一挑战，我们引入了高斯森林建模框架，该框架以分层的方式将场景表现为一个混合3D高斯的森林。每个混合高斯保留其独特的显式属性，同时与其兄弟高斯共享隐式属性，从而通过显著减少变量来优化参数化。此外，我们设计了适应性增长和修剪策略，确保在复杂区域的详细表达，并显著减少所需高斯的数量。广泛的实验表明，高斯森林不仅保持了可比的速度和质量，还实现了超过10倍的压缩率，标志着在高效场景建模方面的重大进步。