提出SurfPatch框架，实现流场中曲面的精细匹配与探索式可视化。
SurfPatch: Enabling Patch Matching for Exploratory Stream Surface Visualization
- 通过顶点分类-片块匹配-曲面聚类三阶段，自底向上构建曲面连接关系
- 支持稳态与非稳态流场、标量场等多类型数据的曲面探索，提升查询灵活性
- 提供直观交互界面，适合流体可视化研究者和工程分析人员使用
与线基方法相比，面基技术在流场可视化中尚未得到充分研究，主要受限于其放置、速度、感知和评估难题。本文提出SurfPatch框架，支持探索式流面可视化。首先将面放置问题转化为面选择，并从给定流场数据集中追踪大量流面；随后引入三阶段流程：顶点级分类、片块级匹配、面级聚类，分层建立顶点与片块、片块与面之间的关联。该自底向上的方法实现细粒度、多尺度的片块级匹配，显著区别于现有工作仅支持面级匹配，提供了前所未有的查询灵活性。设计了直观可视化界面，使用户可便捷探索流面集合。SurfPatch不仅适用于稳态流场生成的流面，还在稳态与非稳态流场及标量场提取的等值面实验中验证了有效性。代码已开源。
原文摘要 · Abstract (English)
Unlike their line-based counterparts, surface-based techniques have yet to be thoroughly investigated in flow visualization due to their significant placement, speed, perception, and evaluation challenges. This paper presents SurfPatch, a novel framework supporting exploratory stream surface visualization. To begin with, we translate the issue of surface placement to surface selection and trace a large number of stream surfaces from a given flow field dataset. Then, we introduce a three-stage process: vertex-level classification, patch-level matching, and surface-level clustering that hierarchically builds the connection between vertices and patches and between patches and surfaces. This bottom-up approach enables fine-grained, multiscale patch-level matching, sharply contrasts surface-level matching offered by existing works, and provides previously unavailable flexibility during querying. We design an intuitive visual interface for users to conveniently visualize and analyze the underlying collection of stream surfaces in an exploratory manner. SurfPatch is not limited to stream surfaces traced from steady flow datasets. We demonstrate its effectiveness through experiments on stream surfaces produced from steady and unsteady flows as well as isosurfaces extracted from scalar fields. The code is available at https://github.com/adlsn/SurfPatch.
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