针对3DGS模型设计非均匀量化，提升压缩效率
Non-Uniform Quantisation for 3DGS Compression
- 按重要性加权量化，适应数据分布差异
- 通过重要性融合消除体素后冗余，压缩率提升显著
- 兼容点云表示，适合作为3DGS标准压缩方案
3D高斯点阵（3DGS）已成为新视角合成的强大技术，但其高比特率要求对存储和传输带来挑战。为推动实际应用并确保3DGS生态系统的互操作性，标准化压缩格式至关重要。本文提出一种专为3DGS模型设计的新型非均匀量化方案，通过重要性加权量化适应底层数据分布，并利用重要性加权融合消除体素化后的冗余。在基准数据集上的大量评估表明，该方法达到当前最优压缩性能。此外，该方案可兼容任意基于点云的表示形式，旨在作为即将开展的MPEG 3DGS压缩标准化活动的重要贡献。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, yet its high bitrate requirements pose significant challenges for storage and transmission. To enable practical applications and ensure interoperability within the 3DGS ecosystem, standardised compression formats are essential. In this paper, we propose a novel non-uniform quantisation scheme specifically tailored for 3DGS models. Our approach adapts to the underlying data distribution by applying importance-weighted quantisation and eliminating post-voxelisation redundancy through importance weighted merging. Extensive evaluations on benchmark datasets demonstrate that our method achieves state-of-the-art compression performance. Furthermore, the proposed scheme is compatible with any point-cloud-based representation and is intended as a formal contribution to the upcoming MPEG 3DGS compression standardisation activities.
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