| Title: |
Topology Aware Neural Interpolation of Scalar Fields |
| Authors: |
Kissi, Mohamed Amine; Sisouk, Keanu; Levine, Joshua, A; Tierny, Julien |
| Contributors: |
Algorithmes, Programmes et Résolution (APR); LIP6; Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS); Institut des Sciences du Calcul et des Données (ISCD); Sorbonne Université (SU) |
| Source: |
https://topoinvis-workshop.github.io/2025/program.html ; IEEE Workshop on Topological Data Analysis and Visualization ; https://hal.science/hal-05338058 ; IEEE Workshop on Topological Data Analysis and Visualization, Nov 2025, Vienna, Austria. |
| Publisher Information: |
CCSD |
| Publication Year: |
2025 |
| Subject Terms: |
persistence optimization; topological data analysis; neural networks; Temporal interpolation; [INFO]Computer Science [cs] |
| Subject Geographic: |
Vienna; Austria |
| Description: |
International audience ; This paper presents a neural scheme for the topology-aware interpolation of time-varying scalar fields. Given a time-varying sequence of persistence diagrams, along with a sparse temporal sampling of the corresponding scalar fields, denoted as keyframes, our interpolation approach aims at "inverting" the non-keyframe diagrams to produce plausible estimations of the corresponding, missing data. For this, we rely on a neural architecture which learns the relation from a time value to the corresponding scalar field, based on the keyframe examples, and reliably extends this relation to the non-keyframe time steps. We show how augmenting this architecture with specific topological losses exploiting the input diagrams both improves the geometrical and topological reconstruction of the non-keyframe time steps. At query time, given an input time value for which an interpolation is desired, our approach instantaneously produces an output, via a single propagation of the time input through the network. Experiments interpolating 2D and 3D time-varying datasets show our approach superiority, both in terms of data and topological fitting, with regard to reference interpolation schemes. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-05338058; https://hal.science/hal-05338058v1/document; https://hal.science/hal-05338058v1/file/2508.17995v1.pdf |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.93FD2D79 |
| Database: |
BASE |