3D Field Data Reduction with Adaptive Sample-Based Gaussian-Encoded Reconstruction
In scientific simulation, regular grids, unstructured meshes, and particle-based formats are chosen to represent field data for computational efficiency, geometry/adaptive flexibility, and following motion/deformation, respectively. Each of these field data formats is often handled through separate data-specific processing pipelines. We present a unified sample-based Gaussian encoding method that represents these data forms under a single fixed-budget formulation. The method initializes and refines Gaussian primitives directly from the input samples while preserving a prescribed primitive count and encoded size to achieve a desired level of data reduction. Across structured, unstructured,...
| Authors | Michael R. Martin, Joseph Insley, Victor A. Mateevitsi et al. |
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| Primary category | cs.GR |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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