Existing 3D Gaussian Splatting simplification methods commonly use importance scores, such as blending weights or sensitivity, to identify redundant Gaussians. However, these scores are not driven by visual error metrics, often leading to suboptimal trade-offs between compactness and rendering fidelity. We present GaussianPOP, a principled simplification framework based on analytical Gaussian error quantification. Our key contribution is a novel error criterion, derived directly from the 3DGS rendering equation, that precisely measures each Gaussian's contribution to the rendered image. By introducing a highly efficient algorithm, our framework enables practical error calculation in a single forward pass. The framework is both accurate and flexible, supporting on-training pruning as well as post-training simplification via iterative error re-quantification for improved stability. Experimental results show that our method consistently outperforms existing state-of-the-art pruning methods across both application scenarios, achieving a superior trade-off between model compactness and high rendering quality.

翻译：现有3D高斯溅射简化方法通常采用混合权重或敏感度等重要性评分来识别冗余高斯单元。然而，这些评分并非由视觉误差指标驱动，往往导致模型紧凑性与渲染保真度之间的权衡欠优。本文提出GaussianPOP——一种基于解析式高斯误差量化的原理性简化框架。我们的核心贡献在于推导出一种新颖的误差准则，该准则直接源自3DGS渲染方程，能够精确度量每个高斯单元对渲染图像的贡献度。通过引入高效算法，本框架可在单次前向传播中实现实际误差计算。该框架兼具精确性与灵活性，既支持训练过程中的剪枝操作，也能通过迭代式误差重量化实现训练后简化，从而提升稳定性。实验结果表明，本方法在两种应用场景下均持续优于现有最先进的剪枝方法，在模型紧凑性与高渲染质量之间实现了更优的权衡。