We present Submerse, an end-to-end framework for visualizing flooding scenarios on large and immersive display ecologies. Specifically, we reconstruct a surface mesh from input flood simulation data and generate a to-scale 3D virtual scene by incorporating geographical data such as terrain, textures, buildings, and additional scene objects. To optimize computation and memory performance for large simulation datasets, we discretize the data on an adaptive grid using dynamic quadtrees and support level-of-detail based rendering. Moreover, to provide a perception of flooding direction for a time instance, we animate the surface mesh by synthesizing water waves. As interaction is key for effective decision-making and analysis, we introduce two novel techniques for flood visualization in immersive systems: (1) an automatic scene-navigation method using optimal camera viewpoints generated for marked points-of-interest based on the display layout, and (2) an AR-based focus+context technique using an auxiliary display system. Submerse is developed in collaboration between computer scientists and atmospheric scientists. We evaluate the effectiveness of our system and application by conducting workshops with emergency managers, domain experts, and concerned stakeholders in the Stony Brook Reality Deck, an immersive gigapixel facility, to visualize a superstorm flooding scenario in New York City.

翻译：我们提出Submerse，一个用于在大型沉浸式显示生态中可视化洪水场景的端到端框架。具体而言，我们从输入的洪水模拟数据中重建表面网格，并通过融合地形、纹理、建筑物及其他场景对象等地理数据生成一个按比例缩放的三维虚拟场景。为了优化大规模模拟数据集的计算与内存性能，我们利用动态四叉树在自适应网格上对数据进行离散化，并支持基于细节层次的渲染。此外，为提供某一时刻的洪水方向感知，我们通过合成水波对表面网格进行动画化处理。由于交互对于有效决策与分析至关重要，我们引入了两种适用于沉浸式系统的洪水可视化新技术：（1）一种基于显示布局为标记兴趣点生成最优相机视角的自动场景导航方法，以及（2）一种利用辅助显示系统的基于增强现实的焦点+上下文技术。Submerse由计算机科学家与大气科学家合作开发。我们通过紧急管理人员、领域专家及利益相关者在石溪Reality Deck（一个沉浸式十亿像素设施）中开展研讨会，评估系统与应用的有效性，以可视化纽约市一次超级风暴洪水场景。