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Mattéo Clémot

dblp:356/7802 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2026
0009-0000-2524-0244ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021Theory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Computational geometry · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
dimensionality reduction
1.012026
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction · IEEE Trans. Vis. Comput. Graph. 2026
Computational geometry › topological data analysis
alpha complex
0.912025
When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025
Computational geometry › triangulation
delaunay triangulation
0.912025
When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025
Computational geometry › triangulation
restricted delaunay complex
0.912025
When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025
Computational geometry › geometric modeling and processing › point cloud analysis › geometric reconstruction
shape reconstruction
0.912025
When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025
Computational geometry › topological data analysis
persistent homology
0.312026
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction · IEEE Trans. Vis. Comput. Graph. 2026

Methods — techniques the papers use, named apart from their topics

topological autoencoder · 2.0persistent homology · 2.0cascade distortion · 2.0vertical collapse · 0.9alpha-complex simplification · 0.9
YearPublicationVenuePosition
2026 Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction
abstract
This paper presents a novel topology-aware dimensionality reduction approach aiming at accurately visualizing the cyclic patterns present in high dimensional data. To that end, we build on the Topological Autoencoders (TopoAE) (Moor et al., 2020) formulation. First, we provide a novel theoretical analysis of its associated loss and show that a zero loss indeed induces identical persistence pairs (in high and low dimensions) for the 0-dimensional persistent homology ($\text{PH}^{0}$) of the Rips filtration. We also provide a counter example showing that this property no longer holds for a naive extension of TopoAE to $\text{PH}^{d}$ for $d\geq 1$. Based on this observation, we introduce a novel generalization of TopoAE to 1-dimensional persistent homology ($\text{PH}^{1}$), called TopoAE++, for the accurate generation of cycle-aware planar embeddings, addressing the above failure case. This generalization is based on the notion of cascade distortion, a new penalty term favoring an isometric embedding of the 2-chains filling persistent 1-cycles, hence resulting in more faithful geometrical reconstructions of the 1-cycles in the plane. We further introduce a novel, fast algorithm for the exact computation of $\text{PH}^{}$ for Rips filtrations in the plane, yielding improved runtimes over previously documented topology-aware methods. Our method also achieves a better balance between the topological accuracy, as measured by the Wasserstein distance, and the visual preservation of the cycles in low dimensions.
Mattéo Clémot, Julie Digne, Julien Tierny
IEEE Trans. Vis. Comput. Graph.1
2025 When Alpha-Complexes Collapse onto Codimension-1 Submanifolds
abstract
Given a finite set of points P sampling an unknown smooth surface ℳ ⊆ ℝ³, our goal is to triangulate ℳ based solely on P. Assuming ℳ is a smooth orientable submanifold of codimension 1 in ℝ^d, we introduce a simple algorithm, Naive Squash, which simplifies the α-complex of P by repeatedly applying a new type of collapse called vertical relative to ℳ. Naive Squash also has a practical version that does not require knowledge of ℳ. We establish conditions under which both the naive and practical Squash algorithms output a triangulation of ℳ. We provide a bound on the angle formed by triangles in the α-complex with ℳ, yielding sampling conditions on P that are competitive with existing literature for smooth surfaces embedded in ℝ³, while offering a more compartmentalized proof. As a by-product, we obtain that the restricted Delaunay complex of P triangulates ℳ when ℳ is a smooth surface in ℝ³ under weaker conditions than existing ones.
Dominique Attali, Mattéo Clémot, Bianca B. Dornelas, André Lieutier
SoCG2
2023 Neural skeleton: Implicit neural representation away from the surface
Mattéo Clémot, Julie Digne
Comput. Graph.1