标题： 置信度喷射：通过可学习的贝塔分布进行置信度压缩的3D高斯喷射 显示英文标题

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

摘要： 3D高斯点绘制实现了高质量的实时渲染，但通常会产生数百万个点，导致存储和计算开销过大。 我们提出了一种基于可学习置信度分数的新型有损压缩方法，该方法建模为贝塔分布。 每个点的置信度通过重建感知损失进行优化，从而在保持视觉保真度的同时删除低置信度的点。 所提出的方法与架构无关，可以应用于任何高斯点绘制变体。 此外，平均置信度值作为评估场景质量的新指标。 大量实验表明，与之前的工作相比，该方法在压缩和保真度之间表现出有利的权衡。 我们的代码和数据可在 https://github.com/amirhossein-razlighi/Confident-Splatting 公开获取。 显示英文摘要

摘要： 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