Implementing Persistence-Based Clustering of Point Clouds in the Topology ToolKit

Ryan Cotsakis, Jim Shaw, Julien Tierny, Joshua A. Levine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We show how the scalar field topology features of the Topology ToolKit (TTK) can be leveraged in a pipeline for persistence-based clustering of point clouds. While TTK provides numerous features for computing topological structures of scalar fields on unstructured meshes, prior to this work, it allowed for only basic point cloud input. In this work, we implemented two new modules in TTK: one for sampling scalar fields based on either distance or density of the point cloud and a second for computing persistence-based clusters. Both modules provide heuristics for automatically specifying key thresholds so as to simplify user interaction. This document outlines the implementation details of the two modules and provides experimental results that demonstrate their modularity and utility.

Original languageEnglish (US)
Title of host publicationTopological Methods in Data Analysis and Visualization VI - Theory, Applications, and Software
EditorsIngrid Hotz, Talha Bin Masood, Filip Sadlo, Julien Tierny
PublisherSpringer Science and Business Media Deutschland GmbH
Pages343-357
Number of pages15
ISBN (Print)9783030834999
DOIs
StatePublished - 2021
Externally publishedYes
Event8th Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2019 - Nyköping, Sweden
Duration: Jun 17 2019Jun 19 2019

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

Conference

Conference8th Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2019
Country/TerritorySweden
CityNyköping
Period6/17/196/19/19

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

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