学习3D高斯泼溅中的可微层次结构
Learning Differentiable Hierarchies in 3D Gaussian Splatting
提出可微的层次化高斯表示与调制渲染，使3DGS支持任意数量的LoD渲染和模型剪枝，可直接用于需要动态精度调整的实时渲染与压缩场景。
原文依据 we propose a learning-based Gaussian hierarchy representation that ranks Gaussians by their contribution to the scene
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管3D高斯泼溅（3DGS）在实时渲染中取得了令人瞩目的性能，但其无序的高斯分布使得细节层次（LoD）构建和模型压缩极具挑战性，限制了其在定制场景中的适用性。本文提出一种基于学习的髙斯层次表示，根据高斯对场景的贡献进行排序，从而支持任意高斯数量下的灵活LoD表示。我们首先引入高斯层次结构的统一连续公式与度量。然后，我们提出一种基于可微微分递减阶跃函数的层次调制渲染方法，该方法能够在保持近似等效渲染的同时实现高效的层次学习。此外，我们开发了一种概率密度函数引导的活跃区域采样策略，促使学习到的层次结构在其值域内广泛分布。我们的方法无需额外训练阶段，并且在与经典3DGS相当的训练时间内生成高斯层次结构。在多个数据集上的实验表明，我们的方法在LoD渲染和模型剪枝方面均达到与最优方法相当或超越的性能。
英文原文摘要 1223 字符
Although 3D Gaussian Splatting (3DGS) has achieved impressive performance in real-time rendering, its unordered Gaussians make level-of-detail (LoD) construction and model compression highly challenging, limiting its applicability in customized scenarios.In this work, we propose a learning-based Gaussian hierarchy representation that ranks Gaussians by their contribution to the scene, enabling flexible LoD representations across arbitrary Gaussian counts.We first introduce a unified, continuous formulation and metric for Gaussian hierarchy. Then, we introduce a hierarchy-based modulated rendering method built upon a Differentiable Decreasing Step Function, which enables efficient hierarchy learning while maintaining approximately equivalent rendering. Moreover, we develop a PDF-Guided Active-Region Sampling strategy that encourages the learned hierarchy to become widely distributed within its value range.Our method requires no additional training stages and produces Gaussian hierarchies within training time comparable to classical 3DGS. Experiments on multiple datasets show that our approach achieves performance comparable to or surpassing state-of-the-art methods in both LoD rendering and model pruning.