SurfPatch：为探索性流面可视化实现块匹配
SurfPatch: Enabling Patch Matching for Exploratory Stream Surface Visualization
AI总结：
本文提出 SurfPatch 框架，通过顶点分类、块匹配和曲面聚类的自底向上流程支持多尺度探索性流面可视化，并在稳态、非稳态流及标量场数据上验证有效性。
摘要：
与基于线的技术不同，基于曲面的技术由于在布局、速度、感知和评估方面存在显著挑战，在流场可视化中尚未得到充分研究。本文提出 SurfPatch，这是一种支持探索性流面可视化的新颖框架。首先，我们将曲面布局问题转化为曲面选择问题，并从给定的流场数据集中追踪大量流面。随后，我们引入一个三阶段流程：顶点级分类、块级匹配和曲面级聚类，分层建立顶点与块之间以及块与曲面之间的联系。这种自底向上的方法实现了细粒度、多尺度的块级匹配，与现有工作提供的曲面级匹配形成鲜明对比，并在查询时提供了此前无法获得的灵活性。我们设计了直观的可视化界面，方便用户以探索方式可视化和分析底层流面集合。SurfPatch 并不局限于从稳态流数据集追踪得到的流面。我们通过在稳态流和非稳态流生成的流面以及从标量场提取的等值面上进行实验，证明了其有效性。代码可在 https://github.com/adlsn/SurfPatch 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
