提出无需渲染的高效3D高斯切片剪枝方法，大幅降低计算开销。
REFINE: Super-efficient 3D Gaussian Splatting Pruning via Rendering-Free Primitive Importance
- 基于解析近似赫斯矩阵场，构建无需渲染的感知重要性度量。
- 在多个数据集上实现3000倍计算复杂度降低，设备延迟提速约20倍。
- 适合需要实时3D重建与部署的轻量化应用场景。
现有的3D高斯切片（3DGS）剪枝方法要么导致质量严重下降，要么计算开销过大。本文提出REFINE，一种以新型无渲染原始重要性度量为核心的超高效3DGS剪枝框架。该方法利用解析近似的、感知相关的赫斯矩阵场，量化单个原始体被移除时引起的预期感知误差。通过建模可见性、投影几何和内容自适应超参数的联合调制，完全避免了昂贵的前向渲染过程，推导出一个各向异性的感知权重场，作为原始体重要性的高保真代理。在多个基准数据集上的大量实验表明，REFINE在保持高度竞争的渲染质量的同时，将剪枝相关计算复杂度降低了3000倍，相比最先进剪枝方法实现了约20倍的实际设备延迟加速。
原文摘要 · Abstract (English)
Existing pruning methods for 3D Gaussian splatting (3DGS) suffer from either severe quality degradation or prohibitive computational overhead. In this paper, we propose REFINE, a highly accelerated 3DGS pruning framework centered on a novel rendering-free primitive importance metric. Our approach leverages an analytically approximated, rendering-aware Hessian field to quantify the expected perceptual error induced by the removal of individual primitives. By modeling the joint modulation of visibility, projection geometry and the content adaptive hyperparameter, we entirely bypass costly forward rendering passes and derive an anisotropic perceptual weight field that serves as a high-fidelity proxy for primitive importance. Extensive experiments across multiple benchmark datasets demonstrate that REFINE maintains highly competitive rendering quality while achieving a $3,000\times$ reduction in pruning-related computational complexity, translating to a practical $\sim 20\times$ speedup in device latency compared to state-of-the-art pruning methods.
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