Research · Teaching · Software Quentin Mérigot
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I am a researcher in applied mathematics and computer science working for CNRS at Ceremade, Université Paris-Dauphine. Before this, I was in Laboratoire Jean Kuntzmann in Grenoble.

Research topics: Computational geometry, geometric inference, computational optimal transport. More generally, I'm interested in the discretization of geometric variational and inverse problems.

Address: Ceremade, Université Paris-Dauphine
Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex
E-mail: lastname@ceremade.dauphine.fr

Preprints

  1. Convergence of a Newton algorithm for semi-discrete optimal transport
    J. Kitagawa, Q. Mérigot, B. Thibert
  2. Minimal geodesics along volume preserving maps, through semi-discrete optimal transport
    Q. Mérigot, J.M. Mirebeau

Publications

  1. Discretization of functionals involving the Monge-Ampère operator
    J.D. Benamou, G. Carlier, Q. Mérigot, É. Oudet
    Numerische Mathematik, to appear [doi]
  2. Far-field reflector problem and intersection of paraboloids
    P. Machado Manhães de Castro, Q. Mérigot, B. Thibert
    Numerische Mathematik, to appear [doi] (also Proc. SoCG 2014 [doi])
  3. Measuring the misfit between seismograms using an optimal transport distance: application to full waveform inversion
    L. Métivier, R. Brossier, Q. Mérigot, É. Oudet, J. Virieux
    Geophysical Journal International, 205 (1), 345-377, 2016 [doi]
  4. Handling convexity-like constraints in variational problems
    Q. Mérigot, É. Oudet
    SIAM Journal on Numerical Analysis, 52 (5), 2466–2487, 2014 [doi].
  5. Robust Geometry Estimation using the Generalized Voronoi Covariance Measure
    L. Cuel, J.O. Lachaud, Q. Mérigot, B. Thibert
    SIAM Journal on Imaging Science (SIIMS), 8(2), 1293–1314, 2015 [doi].
  6. On the reconstruction of convex sets from random normals measurements
    H. Abdallah, Q. Mérigot
    Discrete and Computational Geometry 53(3), 569–586, 2015 (also Proc SoCG 2014) [doi]
  7. Discrete optimal transport: complexity, geometry and applications
    Q. Mérigot, É. Oudet
    Discrete and Computational Geometry, 55(2), 263–283, 2016 [doi]
  8. Far-field reflector problem under design constraints
    J. André, D. Attali, Q. Mérigot, B. Thibert
    International Journal of Computational Geometry and Applications (IJCGA), 25 (2), 143-162, 2015 [doi].
  9. Lower bounds for k-distance approximation.
    Q. Mérigot
    Proceedings of the 29th ACM Symposium on Computational Geometry, 2013 [doi]
  10. Shape Matching via Quotient Spaces.
    M. Ovsjanikov, Q. Mérigot, V. Pătrăucean, L. Guibas
    Computer Graphics Forum 32 (5) 1–11, 2013 (also Proc SGP 2013) [doi].
  11. Witnessed k-distance.
    L. Guibas, Q. Mérigot, D. Morozov
    Discrete and Computational Geometry, 49 (1) 22–45, 2013 (also Proc SoCG 2011) [doi].
  12. A multiscale approach to optimal transport.
    Q. Mérigot
    Computer Graphics Forum 30 (5) 1583–1592, 2011 (also Proc SGP 2011) [doi].
  13. Size of the medial axis and stability of Federer’s curvature measures
    Q. Mérigot
    In Optimal Transportation: Theory and Applications, London Mathematical Society Lecture Note Series 413, pp 288–306 [doi]
  14. Geometric inference for probability measures.
    F. Chazal, D. Cohen-Steiner, Q. Mérigot
    Foundations of Computational Mathematics 11, 733-751 (2011) [doi].
  15. Boundary measures for geometric inference.
    F. Chazal, D. Cohen-Steiner, Q. Mérigot
    Foundation of Computational Mathematics 10, 221-240 (2010) [doi].
  16. Feature Preserving Mesh Generation from 3D Point Clouds.
    N. Salman, M. Yvinec, Q. Mérigot
    Computer Graphics Forum 29 (5) 1623–1632, 2010 (also Proc. SGP 2010) [doi].
  17. One Point Isometric Matching with the Heat Kernel.
    M. Ovsjanikov, Q. Mérigot F. Mémoli, L. Guibas
    Computer Graphics Forum 29 (5) 1555–1564, 2010 (also Proc. SGP 2010) [doi].
  18. Voronoi-based Curvature and Feature Estimation from Point Clouds.
    Q. Mérigot, M. Ovsjanikov, L. Guibas
    IEEE Transactions on Visualization and Computer Graphics 17 (6) 743–756, 2011 [doi].
  19. Anosov AdS representations are quasi-Fuchsian.
    T. Barbot, Q. Mérigot
    Groups, Geometry and Dynamics 6(3), 441-483 (2012) [doi].

Notes, thesis, surveys

  1. A comparison of two dual methods for discrete optimal transport.
    Geometric Science of Information, LNCS 8085, 389-396, 2013
    Q. Mérigot
  2. Geometric structure detection in point clouds
    Q.Mérigot
    Thèse de doctorat, Université de Nice Sophia-Antipolis.

Events