Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions
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| Format: | Preprint |
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2025
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| _version_ | 1866909665182351360 |
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| author | Razlighi, AmirHossein Naghi Golezani, Elaheh Badali Kasaei, Shohreh |
| author_facet | Razlighi, AmirHossein Naghi Golezani, Elaheh Badali Kasaei, Shohreh |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_22973 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions Razlighi, AmirHossein Naghi Golezani, Elaheh Badali Kasaei, Shohreh Graphics Computer Vision and Pattern Recognition 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 |
| title | Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.22973 |