Flexible and Probabilistic Topology Tracking with Partial Optimal Transport

TL;DR

This paper introduces a novel probabilistic framework for tracking topological features in time-varying scalar fields using merge trees and partial optimal transport, enabling flexible and stable topology analysis.

Contribution

It proposes modeling merge trees as measure networks and defining a new partial optimal transport-based distance for flexible, probabilistic topology tracking.

Findings

Effective in tracking features in scientific simulations

Provides a stable distance measure for merge trees

Enables probabilistic coupling of features over time

Abstract

In this paper, we present a flexible and probabilistic framework for tracking topological features in time-varying scalar fields using merge trees and partial optimal transport. Merge trees are topological descriptors that record the evolution of connected components in the sublevel sets of scalar fields. We present a new technique for modeling and comparing merge trees using tools from partial optimal transport. In particular, we model a merge tree as a measure network, that is, a network equipped with a probability distribution, and define a notion of distance on the space of merge trees inspired by partial optimal transport. Such a distance offers a new and flexible perspective for encoding intrinsic and extrinsic information in the comparative measures of merge trees. More importantly, it gives rise to a partial matching between topological features in time-varying data, thus enabling flexible topology tracking for scientific simulations. Furthermore, such partial matching may be interpreted as probabilistic coupling between features at adjacent time steps, which gives rise to probabilistic tracking graphs. We derive a stability result for our distance and provide numerous experiments indicating the efficacy of our framework in extracting meaningful feature tracks.