Recent advances in 3D Gaussian Splatting have allowed for real-time, high-fidelity novel view synthesis. Nonetheless, these models have significant storage requirements for large and medium-sized scenes, hindering their deployment over cloud and streaming services. Some of the most recent progressive compression techniques for these models rely on progressive masking and scalar quantization techniques to reduce the bitrate of Gaussian attributes using spatial context models. While effective, scalar quantization may not optimally capture the correlations of high-dimensional feature vectors, which can potentially limit the rate-distortion performance. In this work, we introduce a novel progressive codec for 3D Gaussian Splatting that replaces traditional methods with a more powerful Residual Vector Quantization approach to compress the primitive features. Our key contribution is an auto-regressive entropy model, guided by a multi-resolution hash grid, that accurately predicts the conditional probability of each successive transmitted index, allowing for coarse and refinement layers to be compressed with high efficiency.

翻译：近年来，3D高斯溅射技术的进展实现了实时、高保真的新视角合成。然而，这些模型对于大中型场景具有显著的存储需求，阻碍了其在云和流媒体服务上的部署。针对这些模型的一些最新渐进式压缩技术依赖于渐进式掩码和标量量化技术，利用空间上下文模型来降低高斯属性的比特率。尽管有效，但标量量化可能无法最优地捕捉高维特征向量的相关性，这可能会限制率失真性能。在本工作中，我们提出了一种新颖的3D高斯溅射渐进式编解码器，它用更强大的残差向量量化方法取代传统方法，以压缩原始特征。我们的核心贡献是一个由多分辨率哈希网格引导的自回归熵模型，该模型能够准确预测每个连续传输索引的条件概率，从而允许以高效率压缩粗糙层和细化层。