用可变形高斯点云实现仿真参数的实时灵活探索。
GS-Surrogate: Deformable Gaussian Splatting for Parameter Space Exploration of Ensemble Simulations
- 基于可变形高斯点云构建可视化代理模型，分离仿真与可视化变化。
- 支持实时交互式等值面提取和颜色映射编辑，探索效率显著提升。
- 适合需要快速调整可视化参数的科学仿真研究人员使用。
在多个科学领域，集合仿真参数空间探索日益重要。然而，由于原始数据存储成本高与可视化设置灵活性之间的权衡，灵活的后处理探索仍具挑战性。现有可视化代理模型要么仅在图像空间操作而缺乏显式3D表示，要么依赖计算开销大的神经辐射场，且将所有参数变化编码于单一隐式场中。本文提出GS-Surrogate，一种基于可变形高斯点云的可视化代理模型，用于参数空间探索。方法首先构建一个基准高斯场作为基础3D表示，并通过序列化的参数条件变形进行适应。通过将仿真相关变化与可视化特定变化分离，该显式建模方式实现了对不同可视化任务（如等值面提取、颜色映射编辑）的高效可控适配。我们在多种仿真数据集上评估了该框架，结果表明，GS-Surrogate可在仿真与可视化参数空间中实现实时、灵活的探索。
原文摘要 · Abstract (English)
Exploring ensemble simulations is increasingly important across many scientific domains. However, supporting flexible post-hoc exploration remains challenging due to the trade-off between storing the expensive raw data and flexibly adjusting visualization settings. Existing visualization surrogate models have improved this workflow, but they either operate in image space without an explicit 3D representation or rely on neural radiance fields that are computationally expensive for interactive exploration and encode all parameter-driven variations within a single implicit field. In this work, we introduce GS-Surrogate, a deformable Gaussian Splatting-based visualization surrogate for parameter-space exploration. Our method first constructs a canonical Gaussian field as a base 3D representation and adapts it through sequential parameter-conditioned deformations. By separating simulation-related variations from visualization-specific changes, this explicit formulation enables efficient and controllable adaptation to different visualization tasks, such as isosurface extraction and transfer function editing. We evaluate our framework on a range of simulation datasets, demonstrating that GS-Surrogate enables real-time and flexible exploration across both simulation and visualization parameter spaces.
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