Confident Splatting: Confidence-Based Compression of 3D Gaussian
  Splatting via Learnable Beta Distributions
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
68641fe0d59a9eda590249f7
AmirHossein Naghi Razlighi
claimed_verified
2025-07-02T07:40:20.262Z
648490c2ad9a14dd2ec6960b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/DjtPNSc90hX66EcyRh8qu.png
Amirhossein Razlighi
user
AmirHossein-razlighi
68641fe0d59a9eda590249f8
Elaheh Badali Golezani
68641fe0d59a9eda590249f9
Shohreh Kasaei
Confident Splatting: Confidence-Based Compression of 3D Gaussian
  Splatting via Learnable Beta Distributions
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
A novel lossy compression method using learnable confidence scores improves storage and computational efficiency in 3D Gaussian Splatting without sacrificing visual quality.
Gaussian Splatting learnable confidence scores Beta distributions reconstruction-aware losses pruning visual fidelity architecture-agnostic