RAP: Fast Feedforward Rendering-Free Attribute-Guided Primitive Importance Score Prediction for Efficient 3D Gaussian Splatting Processing

2026-02-23

AI Summary

This work addresses the challenge of redundant Gaussians generated during iterative optimization in 3D Gaussian Splatting (3DGS) reconstruction, which hinders efficient compression, transmission, and simplification. The paper proposes the first rendering-free, feedforward method to directly predict the importance of each Gaussian primitive based solely on its intrinsic attributes and local neighborhood statistics, eliminating reliance on multi-view rendering, visibility computation, or differentiable rasterization. A lightweight MLP network is trained using a composite loss comprising reconstruction error, pruning-aware regularization, and importance distribution constraints. After training on a small set of scenes, the model generalizes effectively to unseen data, offering a plug-and-play solution that significantly enhances processing efficiency across the entire 3DGS pipeline—from reconstruction to compression and transmission.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• 3D Gaussian Splatting
• primitive importance
• rendering-free
• efficient processing
• attribute-guided

Innovation

• rendering-free
• attribute-guided
• primitive importance prediction
• 3D Gaussian Splatting
• feedforward MLP