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May 29, 20260 citationsOpen Access

Singular Arrange and Traverse Algorithm for Computing Reeb Spaces of Bivariate PL Maps

PHPetar HristovLinköping UniversityIHIngrid HotzLinköping UniversityTMTalha Bin MasoodLinköping University

Key Points

  • To develop an exact and efficient algorithm for computing the Reeb space of bivariate piecewise linear (PL) maps.
  • Introduced the singular arrange and traverse algorithm based on the arrange and traverse framework.
  • Focused on the contribution of singular edges while ignoring regular edges.
  • Provided a benchmark against the original arrange and traverse algorithm.
  • Achieved performance gains of up to four orders of magnitude on real-world datasets compared to existing algorithms.

Abstract

We present an exact and efficient algorithm for computing the Reeb space of a bivariate PL map. The Reeb space is a topological structure that generalizes the Reeb graph to the setting of multiple scalar-valued functions defined over a shared domain, a situation that frequently arises in practical applications. While the Reeb graph has become a standard tool in computer graphics, shape analysis, and scientific visualization, the Reeb space is still in the early stages of adoption. Although several algorithms for computing the Reeb space have been proposed, none offer an implementation that is both exact and efficient, which has substantially limited its practical use. To address this gap, we introduce singular arrange and traverse, a new algorithm built upon the arrange and traverse framework Hristov et al., 2025. Our method exploits the fact that, in the bivariate case, only singular edges contribute to the structure of Reeb space, allowing us to ignore many regular edges Tierny and Carr, 2017. This observation results in substantial efficiency gains on datasets where most edges are regular, which is common in many numerical simulations of physical systems. We provide an implementation of our method and benchmark it against the original arrange and traverse algorithm, showing performance gains of up to four orders of magnitude on real-world datasets.

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Cite This Study

Hristov et al. (2026) studied this question.

synapsesocial.com/papers/6a192d4afab5b468c44162e6https://doi.org/10.4230/lipics.socg.2026.57
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