用一个神经网络压缩上百变量科学数据，提升可视化与存储效率
Compressive Modeling and Visualization of Multivariate Scientific Data using Implicit Neural Representation
- 用共享参数的隐式神经网络统一建模多变量数据
- 在数据重建、渲染质量、变量依赖保持上达当前最优
- 适合需要高效处理高维科学数据的研究者
深度神经网络在科学可视化任务中日益普及。近期基于隐式神经表示的压缩数据模型在时空体数据可视化和超分辨率任务中表现优异。受此启发，我们为包含数十至数百个变量的多变量数据集构建了压缩神经表示。该方法通过参数共享，使用单一网络同时学习所有变量的表示，实现了领先的压缩效果。全面评估表明，该方法在数据重建质量、渲染与可视化质量、变量间依赖信息保留以及存储效率方面均表现卓越。
原文摘要 · Abstract (English)
The extensive adoption of Deep Neural Networks has led to their increased utilization in challenging scientific visualization tasks. Recent advancements in building compressed data models using implicit neural representations have shown promising results for tasks like spatiotemporal volume visualization and super-resolution. Inspired by these successes, we develop compressed neural representations for multivariate datasets containing tens to hundreds of variables. Our approach utilizes a single network to learn representations for all data variables simultaneously through parameter sharing. This allows us to achieve state-of-the-art data compression. Through comprehensive evaluations, we demonstrate superior performance in terms of reconstructed data quality, rendering and visualization quality, preservation of dependency information among variables, and storage efficiency.
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