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
this https URL

介绍了高斯森林建模框架，该框架通过在复杂区域中进行详细表示并显著减少所需的高斯模型数量，从而实现了与现有方法相媲美的速度和质量，并且在压缩比方面取得了 10 倍以上的显著进展。