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 sensitive to the number and selection of views, rely on specialized differentiable rasterizers, and have long calculation times that grow linearly with view count, making them difficult to integrate as plug-and-play modules and limiting 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 rendering-based or visibility-dependent computations. A compact MLP predicts per-primitive importance scores using rendering loss, pruning-aware loss, and significance distribution regularization. After training on a small set of scenes, RAP generalizes effectively to unseen data and can be seamlessly integrated into reconstruction, compression, and transmission pipelines. Our code is publicly available at https://github.com/yyyykf/RAP.

翻译：三维高斯溅射（3DGS）已成为高质量三维场景重建的前沿技术。然而，其迭代优化与致密化过程会生成大量基元，且每个基元对重建的贡献程度存在显著差异。因此，评估基元重要性对于在重建过程中消除冗余、实现高效压缩与传输至关重要。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进行评估。但此类方法对视角数量与选取敏感，需依赖专用可微分光栅化器，且计算时长随视角数量线性增长，导致其难以作为即插即用模块集成，限制了可扩展性与泛化能力。为解决这些问题，我们提出RAP——一种快速前馈免渲染属性引导方法，用于高效预测3DGS中的重要性评分。RAP直接依据高斯属性本征特征与局部邻域统计量推断基元显著性，避免了基于渲染或依赖可见性的计算。通过一个紧凑的MLP，结合渲染损失、剪枝感知损失与显著性分布正则化，预测各基元的重要性评分。在少量场景上训练后，RAP能有效泛化至未见数据，并可无缝集成到重建、压缩与传输流程中。我们的代码公开于 https://github.com/yyyykf/RAP。