SurfPatch: Enabling Patch Matching for Exploratory Stream Surface Visualization

2025-01-01

AI Summary

Existing face-based flow visualization methods suffer from limited interactivity, explorability, and multi-scale analytical capability. To address these limitations, this paper proposes a multi-scale hierarchical matching framework for flow surface analysis. It introduces, for the first time, a patch-level matching mechanism—overcoming the constraints of conventional surface-level matching—and enables bottom-up associative modeling across vertices, patches, and global surfaces. The framework integrates flow surface tracing, vertex classification, patch matching, surface clustering, and interactive visualization to support fine-grained, queryable flow surface exploration. It further extends to unsteady flows and scalar-field sketch surfaces. Evaluations on diverse steady and unsteady flow datasets, as well as scalar fields, demonstrate significant improvements in flow surface discovery efficiency and analytical flexibility. The implementation is publicly available.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• Flow Visualization
• Water Surface Flow
• Perception and Evaluation

Innovation

• SurfPatch
• Flow Visualization
• User-friendly Interface