Compressive Modeling and Visualization of Multivariate Scientific Data using Implicit Neural Representation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918162560188416 |
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| author | Dwivedi, Abhay Kumar Saklani, Shanu Dutta, Soumya |
| author_facet | Dwivedi, Abhay Kumar Saklani, Shanu Dutta, Soumya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compressive Modeling and Visualization of Multivariate Scientific Data using Implicit Neural Representation Dwivedi, Abhay Kumar Saklani, Shanu Dutta, Soumya Machine Learning Graphics I.3; I.4; I.2 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. |
| title | Compressive Modeling and Visualization of Multivariate Scientific Data using Implicit Neural Representation |
| topic | Machine Learning Graphics I.3; I.4; I.2 |
| url | https://arxiv.org/abs/2510.15535 |