用视觉误差精准量化高斯贡献，实现更小体积更高画质的3D高斯泼溅。
GaussianPOP: Principled Simplification Framework for Compact 3D Gaussian Splatting via Error Quantification
- 基于渲染方程推导误差准则，直接衡量每个高斯对图像的贡献。
- 单次前向传播完成误差计算，支持训练中和训练后简化。
- 在模型压缩与画质间取得更好平衡，适合需要高效3D重建的场景。
现有3D高斯泼溅简化方法多依赖混合权重或敏感度等重要性评分，但这些评分未基于视觉误差指标，常导致紧凑性与渲染保真度之间的权衡不佳。我们提出GaussianPOP，一种基于解析高斯误差量化的原则性简化框架。核心贡献是通过3DGS渲染方程推导出的新误差准则，可精确测量每个高斯对最终图像的贡献。通过引入高效算法，该框架可在单次前向传播中实现实际误差计算。方法兼具准确性和灵活性，支持训练中剪枝及训练后通过迭代误差重量化进行简化，提升稳定性。实验表明，本方法在两种应用场景下均持续优于现有最先进剪枝方法，在模型紧凑性与高渲染质量之间实现更优平衡。
原文摘要 · Abstract (English)
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.
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