Fixed-Budget Gaussian Volume Encoding with Structure-Aware Allocation

2026-08-14

AI Summary

This study addresses data storage and transmission bottlenecks in scientific volumetric simulations alongside resource constraints in in-situ reduction by proposing a fixed-budget encoding framework based on anisotropic Gaussian primitives. Through structure-aware parsing allocation and truncation-aware evaluation, the method enables direct optimization of primitive parameters, supporting multi-state post-processing visualization without dynamic addition or deletion. At the billion-voxel scale, the approach achieves encoding in merely four minutes with a compression ratio exceeding 40,000:1 and a PSNR of 38.7 dB, while delivering up to 51× encoding acceleration. These results effectively overcome critical technical barriers to efficient compression and real-time rendering of massive volumetric datasets, offering a scalable solution for high-fidelity scientific visualization under stringent computational budgets.

Abstract

Scientific simulations often produce scalar volumes faster than they can be stored, transferred, and loaded, while in situ reduction must use only a limited share of simulation resources. This work encodes scalar fields as anisotropic Gaussian primitives under a fixed budget. The complete primitive set is allocated analytically from local field structure, including position, orientation, and shape, then refined directly against the scalar field without densification, pruning, or count changes. The selected budget determines encoded storage before refinement and, together with the iteration schedule, provides a controllable refinement-time budget. In a controlled benchmark, truncation-aware field evaluation reduces encoding time by up to 51x; 1.4 million Gaussians encode a billion-voxel volume in at most four minutes on one desktop GPU, with reduced-iteration refinement completing in under one minute. Across five datasets spanning 2.1 million to 1.1 billion evaluated voxels, compression-useful configurations achieve 15.0-38.7 dB PSNR at compression ratios from 2.2x to over 40,000x. Pre-encoding structure statistics characterize fields for which one-shot allocation yields limited gains from additional capacity. Because primitives retain scalar attributes rather than baked appearance, a single compact model serves every subsequent visualization state - supporting post-hoc transfer-function, colormap, lighting, and viewpoint changes without re-encoding.

Problem

• Scientific visualization
• Volume compression
• Fixed-budget encoding
• In situ reduction
• Scalar field

Innovation

• Fixed-Budget Encoding
• Anisotropic Gaussian Primitives
• Structure-Aware Allocation
• Truncation-Aware Evaluation
• Post-Hoc Visualization