自信噴濺：基於可學習Beta分佈的3D高斯噴濺置信度壓縮

摘要

3D高斯潑濺技術雖能實現高品質的即時渲染，但通常會產生數百萬個潑濺點，導致過度的儲存與計算開銷。我們提出了一種基於可學習置信度分數的新型有損壓縮方法，這些分數被建模為Beta分佈。每個潑濺點的置信度通過重建感知的損失函數進行優化，從而能夠在保持視覺保真度的同時，剔除低置信度的潑濺點。所提出的方法與架構無關，可應用於任何高斯潑濺變體。此外，平均置信度值可作為評估場景品質的新指標。大量實驗表明，與先前工作相比，該方法在壓縮與保真度之間達到了更優的平衡。我們的程式碼與資料已公開於https://github.com/amirhossein-razlighi/Confident-Splatting。

English

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

自信噴濺：基於可學習Beta分佈的3D高斯噴濺置信度壓縮

Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions

摘要

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