PCGS：3D高斯泼溅的渐进式压缩
PCGS: Progressive Compression of 3D Gaussian Splatting
AI总结：
PCGS通过渐进式掩码策略控制高斯数量、渐进式量化控制质量，并利用已有量化结果优化熵编码，在保持与最先进非渐进式方法相当压缩性能的同时，为按需应用实现了3D高斯泼溅的渐进式压缩。
摘要：
3D高斯泼溅（3DGS）在新视角合成中实现了令人印象深刻的渲染保真度和速度。然而，其庞大的数据规模对实际应用构成了重大挑战。尽管已经提出了许多压缩技术，但由于缺乏渐进性，它们无法在按需应用中高效利用现有比特流，导致资源浪费。为了解决这一问题，我们提出了PCGS（3D高斯泼溅的渐进式压缩），它自适应地控制高斯（或锚点）的数量和质量，从而为按需应用实现有效的渐进性。具体而言，在数量方面，我们引入了一种渐进式掩码策略，逐步纳入新的锚点，同时细化现有锚点以提升保真度。在质量方面，我们提出了一种渐进式量化方法，逐步减小量化步长，以实现对高斯属性的更精细建模。此外，为了压缩增量比特流，我们利用已有的量化结果来细化概率预测，从而提高跨渐进级别的熵编码效率。总体而言，PCGS在实现渐进性的同时，保持了与最先进的非渐进式方法相当的压缩性能。代码可在github.com/YihangChen-ee/PCGS获取。
英文摘要：
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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