HEMGS：一种用于3D高斯泼溅数据压缩的混合熵模型
HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression
AI总结：
提出HEMGS混合熵模型，结合可变速率预测器、超先验和自回归网络，实现3DGS数据的有损-无损混合压缩，在保持渲染质量下平均减少约40%存储。
摘要：
在这项工作中，我们提出了一种针对3D高斯泼溅（3DGS）数据的新型压缩框架。基于锚点式3DGS方法，我们的方法通过引入一种用于3D高斯泼溅的新型混合熵模型（HEMGS），压缩每个锚点内的所有属性，以实现混合有损-无损压缩。该模型由三个主要组件组成：可变速率预测器、超先验网络和自回归网络。首先，与以往采用多个模型实现多速率有损压缩从而增加训练开销的方法不同，我们的可变速率预测器通过生成一个学习到的量化步长特征，实现使用单一模型和超参数λ的可变速率压缩，用于多功能有损压缩。其次，为了改进无损压缩，超先验网络捕获场景无关和场景特定的特征以生成先验特征，而自回归网络采用具有灵活感受野的自适应上下文选择算法以生成上下文特征。通过整合这两个特征，HEMGS能够准确估计每个属性中当前编码元素的分布，从而实现改进的熵编码并减少存储。我们将HEMGS集成到一个压缩框架中，在四个基准上的实验结果表明，与基线方法相比，HEMGS在保持渲染质量的同时实现了约40%的平均尺寸缩减，并达到了最先进的压缩结果。
英文摘要：
In this work, we propose a novel compression framework for 3D Gaussian Splatting (3DGS) data. Building on anchor-based 3DGS methodologies, our approach compresses all attributes within each anchor by introducing a novel Hybrid Entropy Model for 3D Gaussian Splatting (HEMGS) to achieve hybrid lossy-lossless compression. It consists of three main components: a variable-rate predictor, a hyperprior network, and an autoregressive network. First, unlike previous methods that adopt multiple models to achieve multi-rate lossy compression, thereby increasing training overhead, our variable-rate predictor enables variable-rate compression with a single model and a hyperparameter $λ$ by producing a learned Quantization Step feature for versatile lossy compression. Second, to improve lossless compression, the hyperprior network captures both scene-agnostic and scene-specific features to generate a prior feature, while the autoregressive network employs an adaptive context selection algorithm with flexible receptive fields to produce a contextual feature. By integrating these two features, HEMGS can accurately estimate the distribution of the current coding element within each attribute, enabling improved entropy coding and reduced storage. We integrate HEMGS into a compression framework, and experimental results on four benchmarks indicate that HEMGS achieves about a 40% average reduction in size while maintaining rendering quality over baseline methods and achieving state-of-the-art compression results.
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