自信飞溅：基于置信度的3D高斯飞溅压缩技术——通过可学习的Beta分布实现

摘要

三维高斯溅射技术虽能实现高质量的实时渲染，但常生成数百万个溅射点，导致存储与计算开销过大。我们提出了一种基于可学习置信度评分的新型有损压缩方法，该评分以Beta分布建模。通过重建感知的损失函数优化每个溅射点的置信度，从而在保持视觉保真度的同时，剔除低置信度的溅射点。所提方法架构无关，可应用于任何高斯溅射变体。此外，平均置信度值可作为评估场景质量的新指标。大量实验表明，与先前工作相比，该方法在压缩与保真度之间取得了更优的权衡。我们的代码与数据已公开于https://github.com/amirhossein-razlighi/Confident-Splatting。

English

3D Gaussian Splatting enables high-quality real-time rendering but often produces millions of splats, resulting in excessive storage and computational overhead. We propose a novel lossy compression method based on learnable confidence scores modeled as Beta distributions. Each splat's confidence is optimized through reconstruction-aware losses, enabling pruning of low-confidence splats while preserving visual fidelity. The proposed approach is architecture-agnostic and can be applied to any Gaussian Splatting variant. In addition, the average confidence values serve as a new metric to assess the quality of the scene. Extensive experiments demonstrate favorable trade-offs between compression and fidelity compared to prior work. Our code and data are publicly available at https://github.com/amirhossein-razlighi/Confident-Splatting

自信飞溅：基于置信度的3D高斯飞溅压缩技术——通过可学习的Beta分布实现

Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions

摘要

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