Recent advances in novel view synthesis have enabled real-time rendering speeds with high reconstruction accuracy. 3D Gaussian Splatting (3D-GS), a foundational point-based parametric 3D scene representation, models scenes as large sets of 3D Gaussians. However, complex scenes can consist of millions of Gaussians, resulting in high storage and memory requirements that limit the viability of 3D-GS on devices with limited resources. Current techniques for compressing these pretrained models by pruning Gaussians rely on combining heuristics to determine which Gaussians to remove. At high compression ratios, these pruned scenes suffer from heavy degradation of visual fidelity and loss of foreground details. In this paper, we propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios than existing approaches. It is computed as a second-order approximation of the reconstruction error on the training views with respect to the spatial parameters of each Gaussian. Additionally, we propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline. After pruning 90% of Gaussians, a substantially higher percentage than previous methods, our PUP 3D-GS pipeline increases average rendering speed by 3.56$\times$ while retaining more salient foreground information and achieving higher image quality metrics than existing techniques on scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets.

翻译：新视角合成领域的最新进展已能实现高重建精度的实时渲染速度。3D高斯泼溅作为一种基于点的参数化3D场景基础表示方法，将场景建模为大规模的3D高斯分布集合。然而，复杂场景可能包含数百万个高斯分布，导致高昂的存储与内存需求，从而限制了3D-GS在资源受限设备上的适用性。现有针对预训练模型的压缩技术通过剪枝高斯分布来实现，其依赖于组合启发式方法来确定需要移除的高斯分布。在高压缩比下，这些经过剪枝的场景会遭受视觉保真度的严重下降和前景细节的丢失。本文提出一种基于原理的敏感度剪枝评分方法，能够在比现有方法显著更高的压缩比下保持视觉保真度与前景细节。该评分通过计算训练视图重建误差关于每个高斯分布空间参数的二阶近似来获得。此外，我们提出一种多轮剪枝-精化流程，该流程可应用于任何预训练的3D-GS模型而无需改变其训练流程。在剪除90%的高斯分布（该比例远超先前方法）后，我们的PUP 3D-GS流程在Mip-NeRF 360、Tanks & Temples和Deep Blending数据集场景上，将平均渲染速度提升3.56$\times$，同时保留了更显著的前景信息，并获得了比现有技术更高的图像质量指标。