mlsanityData to 7 Oct 2026

The Topology ToolKit

arXiv 1805.09110 ↗First posted 22 May 2018cs.GR cs.CG cs.CVcited by 39

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Abstract (arXiv)

This system paper presents the Topology ToolKit (TTK), a software platform designed for topological data analysis in scientific visualization. TTK provides a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependence-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2018.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 5; 2024: 11; 2025: 7; 2026: 9.

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  1. 2021Wasserstein Distances, Geodesics and Barycenters of Merge Treescited by 16
  2. 2022Discrete Morse Sandwich: Fast Computation of Persistence Diagrams for Scalar Data -- An Algorithm and A Benchmarkcited by 11
  3. 2022Topological Analysis of Ensembles of Hydrodynamic Turbulent Flows -- An Experimental Studycited by 11
  4. 2022Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams)cited by 8
  5. 2023Wasserstein Dictionaries of Persistence Diagramscited by 4
  6. 2023Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imagingcited by 3
  7. 2023Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)cited by 3
  8. 2023TTK is Getting MPI-Readycited by 3
  9. 2024ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysiscited by 3
  10. 2024AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithmcited by 3
  11. 2024Evaluating Loss Landscapes from a Topology Perspectivecited by 3
  12. 2025Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reductioncited by 3
27 more
  1. 2026TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualizationcited by 3
  2. 2021A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clusteringcited by 2
  3. 2024Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagationcited by 2
  4. 2024Rapid and Precise Topological Comparison with Merge Tree Neural Networkscited by 2
  5. 2024Time-varying Extremum Graphscited by 2
  6. 2024Jacobi Set Simplification for Tracking Topological Features in Time-Varying Scalar Fieldscited by 2
  7. 2025BondMatcher: H-Bond Stability Analysis in Molecular Systemscited by 2
  8. 2023Visualizing Topological Importance: A Class-Driven Approachcited by 1
  9. 2024Train-Free Segmentation in MRI with Cubical Persistent Homologycited by 1
  10. 2024A Practical Solver for Scalar Data Topological Simplificationcited by 1
  11. 2025Extracting Complex Topology from Multivariate Functional Approximation: Contours, Jacobi Sets, and Ridge-Valley Graphscited by 1
  12. 2026SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualizationcited by 1
  13. 2026HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualizationcited by 1
  14. 2022ReViVD: Exploration and Filtering of Trajectories in an Immersive Environment using 3D Shapescited by 0
  15. 2022Some equivalence relation between persistent homology and morphological dynamicscited by 0
  16. 2024Enabling Fast and Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mappingcited by 0
  17. 2024Identifying Locally Turbulent Vortices within Instabilitiescited by 0
  18. 2025A Scalable System for Visual Analysis of Ocean Datacited by 0
  19. 2025Topology Aware Neural Interpolation of Scalar Fieldscited by 0
  20. 2025Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Datacited by 0
  21. 2025Continuous Edit Distance, Geodesics and Barycenters of Time-varying Persistence Diagramscited by 0
  22. 2026Revisiting the Sliced Wasserstein Kernel for persistence diagrams: a Figalli-Gigli approachcited by 0
  23. 2026MS-COOT: Comparing Morse-Smale Complexes with Co-Optimal Transportcited by 0
  24. 2026DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imagingcited by 0
  25. 2026TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learningcited by 0
  26. 2026Sierpi'nski--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximationcited by 0
  27. 2026Direct Topology Tracking in Continuous Implicit Modelscited by 0