三维场数据缩减：基于自适应采样高斯编码的重建
3D Field Data Reduction with Adaptive Sample-Based Gaussian-Encoded Reconstruction
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中文总结 AI 辅助
提出统一固定预算的基于采样的高斯编码框架，以更少原语实现更高重建精度（PSNR提升4.8dB，原语减少44倍），并利用时间热启动提升时变数据编码效率。
中文摘要 AI 辅助
在科学模拟中，规则网格、非结构化网格和基于粒子的格式分别被选择来表示场数据，以追求计算效率、几何/自适应灵活性以及跟随运动/变形。这些场数据格式中的每一种通常都通过各自独立的数据专用处理流程来处理。我们提出了一种统一的基于采样的高斯编码方法，该方法在单一固定预算公式下表示这些数据形式。该方法直接从输入样本初始化并细化高斯原语，同时保持规定的原语数量和编码大小，以实现期望的数据缩减水平。在结构化、非结构化和粒子数据上，与先前的公式相比，基于采样的公式以可测量的更少原语提高了重建精度，实现了高达4.8 dB的PSNR提升，原语数量约减少44倍。对于时变数据，从先前时间步热启动可将达到独立训练重建质量所需的优化减少。这些结果共同展示了一个统一的固定预算高斯编码框架，适用于结构化、粒子、非结构化和时变科学数据，具有可预测的存储、更高的重建精度和改进的时间编码效率。
英文摘要
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, and particle data, the sample-based formulation improves reconstruction accuracy with measurably fewer primitives in comparison to prior formulations, achieving up to 4.8 dB higher PSNR with an approximate 44x reduction in primitive count. For time-varying data, warm-starting from the previous timestep reduces the optimization required to reach independently trained reconstruction quality. Together, these results demonstrate a unified fixed-budget Gaussian encoding framework for structured, particle, unstructured, and time-varying scientific data with predictable storage, higher reconstruction accuracy, and improved temporal encoding efficiency.
发表机构
- University of California, Davis（加州大学戴维斯分校）
- Argonne National Laboratory（阿贡国家实验室）
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