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
@article{arxiv.2506.22973,
title = {Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions},
author = {AmirHossein Naghi Razlighi and Elaheh Badali Golezani and Shohreh Kasaei},
journal= {arXiv preprint arXiv:2506.22973},
year = {2025}
}