RAP：面向高效 3D 高斯泼溅处理的快速前馈无渲染属性引导基元重要性分数预测
RAP: Fast Feedforward Rendering-Free Attribute-Guided Primitive Importance Score Prediction for Efficient 3D Gaussian Splatting Processing
用轻量 MLP 从高斯本征属性与局部邻域统计直接回归每个基元的重要性分数，完全绕开多视角渲染与可微光栅化，可作为即插即用剪枝模块嵌入 3DGS 重建、压缩与传输流程，替代渲染式贡献评估。
原文依据 RAP infers primitive significance directly from intrinsic Gaussian attributes and local neighborhood statistics, avoiding any rendering-based or visibility-dependent computations.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论3D 高斯泼溅（3DGS）已成为高质量三维场景重建的主流技术。然而，其迭代细化与致密化过程会产生大量基元，每个基元对重建的贡献程度差异显著。因此，估计基元重要性至关重要，既可用于在重建过程中去除冗余，也可支撑高效的压缩与传输。现有方法通常依赖基于渲染的分析，即通过每个基元在多个相机视角下的贡献对其进行评估。但这类方法存在以下问题：1）对视角的数量与选取敏感；2）依赖专门的可微光栅化器；3）计算耗时长且随视角数线性增长，因而难以作为即插即用模块集成，也导致可扩展性与泛化能力受限。为解决上述问题，我们提出 RAP——一种用于 3DGS 中高效重要性分数预测的快速前馈、无渲染、属性引导方法。RAP 直接从高斯本征属性与局部邻域统计推断基元显著性，避免任何基于渲染或依赖可见性的计算。我们训练一个紧凑的 MLP，结合渲染损失、剪枝感知损失与显著性分布正则化损失来预测逐基元的重要性分数。在少量场景上训练后，RAP 可有效泛化到未见数据，并能无缝集成到重建、压缩与传输流程中，提供统一且高效的剪枝方案。
英文原文摘要 1680 字符
3D Gaussian Splatting (3DGS) has emerged as a leading technology for high-quality 3D scene reconstruction. However, the iterative refinement and densification process leads to the generation of a large number of primitives, each contributing to the reconstruction to a substantially different extent. Estimating primitive importance is thus crucial, both for removing redundancy during reconstruction and for enabling efficient compression and transmission.Existing methods typically rely on rendering-based analyses, where each primitive is evaluated through its contribution across multiple camera viewpoints. However, such methods are 1) sensitive to the number and selection of views; 2) rely on specialized differentiable rasterizers; and 3) have long calculation times that grow linearly with view count, making them difficult to integrate as plug-and-play modules, as well as resulting in limited scalability and generalization.To address these issues, we propose RAP -- a fast feedforward Rendering-free Attribute-guided method for efficient importance score Prediction in 3DGS. RAP infers primitive significance directly from intrinsic Gaussian attributes and local neighborhood statistics, avoiding any rendering-based or visibility-dependent computations. A compact MLP is trained to predict per-primitive importance scores using a combination of rendering loss, pruning-aware loss, and significance distribution regularization loss. After being trained on a small set of scenes, RAP generalizes effectively to unseen data and can be seamlessly integrated into reconstruction, compression, and transmission pipelines, providing a unified and efficient pruning solution.