提出可渐进压缩3D高斯点云的新方法，适合按需加载场景数据。
PCGS: Progressive Compression of 3D Gaussian Splatting
- 通过渐进掩码逐步添加新高斯点并优化旧点，控制数量与质量。
- 渐进量化降低量化步长，实现属性建模精度逐级提升。
- 利用已有量化结果优化概率预测，提高多层级压缩效率。
3D高斯点云（3DGS）在新视角合成中实现了出色的渲染保真度和速度，但其庞大的数据量给实际应用带来挑战。尽管已有多种压缩技术，但由于缺乏渐进性，在按需应用中无法有效利用现有比特流，造成资源浪费。为此，我们提出PCGS（3D高斯点云的渐进压缩），自适应控制高斯点的数量与质量，以支持按需应用的高效渐进性。具体而言，针对数量，引入渐进掩码策略，逐步融合新锚点并优化已有锚点以提升保真度；针对质量，提出渐进量化方法，逐步减小量化步长，实现对高斯属性更精细的建模。此外，为压缩增量比特流，利用已有量化结果改进概率预测，提升各渐进层级的熵编码效率。整体上，PCGS在保持与当前最优非渐进方法相当压缩性能的同时，实现了真正的渐进性。代码已开源：github.com/YihangChen-ee/PCGS。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) achieves impressive rendering fidelity and speed for novel view synthesis. However, its substantial data size poses a significant challenge for practical applications. While many compression techniques have been proposed, they fail to efficiently utilize existing bitstreams in on-demand applications due to their lack of progressivity, leading to a waste of resource. To address this issue, we propose PCGS (Progressive Compression of 3D Gaussian Splatting), which adaptively controls both the quantity and quality of Gaussians (or anchors) to enable effective progressivity for on-demand applications. Specifically, for quantity, we introduce a progressive masking strategy that incrementally incorporates new anchors while refining existing ones to enhance fidelity. For quality, we propose a progressive quantization approach that gradually reduces quantization step sizes to achieve finer modeling of Gaussian attributes. Furthermore, to compact the incremental bitstreams, we leverage existing quantization results to refine probability prediction, improving entropy coding efficiency across progressive levels. Overall, PCGS achieves progressivity while maintaining compression performance comparable to SoTA non-progressive methods. Code available at: github.com/YihangChen-ee/PCGS.
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