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 optimizes 3D Gaussian Splatting for reduced storage and computational overhead while maintaining visual fidelity.
Gaussian Splatting lossy compression learnable confidence scores Beta distributions reconstruction-aware losses pruning visual fidelity architecture-agnostic average confidence values