UGINR: large-scale unstructured grid reduction via implicit neural representation

Abstract

Recently, implicit neural representations (INRs) have demonstrated significant capabilities in handling 3D volume data, especially in the context of data compression. However, the majority of research has predominantly focused on structured grids, which are not commonly found in scientific domains, particularly in physics. To address this limitation, we propose an unstructured grid reduction method via implicit neural representation (UGINR). UGINR employs a divide-and-conquer approach; specifically, we segment the large-scale data into pieces based on values. Subsequently, we employ an INR network for each piece to learn its distinctive features. Finally, we integrate these individual networks to achieve the compression goal. To ensure compatibility with established research methods, we sample only the vertices of each cell in the unstructured grid. Through weight quantization, our model can achieve a high compression ratio. To illustrate the effectiveness of the proposed method, we conduct experiments on various datasets, demonstrating our approach’s robustness in scientific visualization and large-scale data compression.

Graphical abstract