这篇论文用3D高斯替代隐式网络来压缩体数据，速度更快、省去网格存储，对非结构化数据尤其厉害。
本文提出一种基于3D高斯原语的显式模型用于体数据压缩。该模型将3D高斯集合重新解释为标量场的显式表示。采用采样策略通过加权聚合重建标量值，并开发了CUDA加速的采样管线。与隐式神经表示相比，在结构化体数据上实现有竞争力的重建质量和显著训练加速，在非结构化体数据上全面优于现有方法。
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint. However, as existing INRs for unstructured volumes do not encode geometry, they require partial mesh storage for later sampling, limiting achievable compression. At the same time, novel view synthesis methods have shown that explicit collections of 3D Gaussians can be used to accurately visualize volume data. In this work, we introduce an explicit model for volume data compression based on 3D Gaussian primitives. We reinterpret collections of 3D Gaussians as an explicit representation of a scalar field and use a sampling strategy that reconstructs scalar values at spatial locations through weighted aggregation of intersecting Gaussians. We develop optimized CUDA-accelerated pipelines for structured and unstructured model sampling, loss functions that encourage accurate domain encoding by our models, and a novel sampling-error based densification strategy. Our explicit formulation naturally encodes domain geometry, eliminating the need for mesh storage in unstructured volumes and introducing significantly higher compression opportunities. Compared to existing INRs, we demonstrate that our explicit model achieves competitive reconstruction quality with significant training speedups on structured volumes, while markedly outperforming in all metrics on unstructured volumes.