Confident Splatting：基于可学习Beta分布置信度的3D高斯泼溅压缩
Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions
AI总结：
提出一种基于可学习Beta分布置信度的3D高斯泼溅有损压缩方法，通过重建感知损失优化置信度以修剪低置信度泼溅，在保持视觉保真度的同时显著降低存储与计算开销。
摘要：
3D高斯泼溅支持高质量实时渲染，但常生成数百万个泼溅，导致过度的存储和计算开销。我们提出一种基于可学习Beta分布置信度的新型有损压缩方法。每个泼溅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修剪低置信度的泼溅。所提方法与架构无关，可应用于任何高斯泼溅变体。此外，置信度值可作为评估场景质量的新指标。大量实验表明，该方法在压缩率与保真度之间取得了优于先前工作的折衷。代码和数据已公开。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
