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

**Research topics:** Computational geometry, geometric inference, numerical optimal transport. More generally, variational and inverse problems with geometric content.

**Address:**Ceremade, Université Paris-Dauphine

Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex

**E-mail:**

*lastname*@ceremade.dauphine.fr

## Events

- 16–20 décembre 2013: Journées de Géométrie Algorithmique 2013, CIRM
- 3–4 octobre 2013: Journées transport optimal et modélisation, Grenoble
- 27–30 mai 2013: Mini-symposium "Géométrie algorithmique et analyse géométrique de données", congrés SMAI 2013, Seignosses. Résumé des exposés.
- I launched and organized the working group
*Calculus of variations, geometry, image*at Laboratoire Jean Kuntzmann in 2012--2013 and 2013--2014.

## Research

**Numerical optimal transport and applications.** The computation of solutions to the discrete version of this problem, that is when one or both measures are sum of Dirac masses, is of interest to me. I implemented a multiscale algorithm to solve the optimal transport problem on the plane and for the squared Euclidean cost based on Kantorovich duality. With J. André, D. Attali, B. Thibert and P. Machado, we are investigating the application of these techniques to solve inverse problems arising in non-imaging optics.

**Geometric reconstruction from noisy data.** With F. Chazal and David Cohen-Steiner, we introduced a notion of distance function *to* a probability measure which can replace the usual distance function in geometric inference. In order to perform global computations, such as computing topology of a certain sublevel set, one needs to approximate this function. This problem raises interesting question in connection with the classical *k*-set problem in computational geometry.

**Distance functions and curvature measures.** The tube formulas assert that the volume of tubular neighborhoods of a manifold contains information about its curvature. Based on a generalization of this idea due to Federer, we proposed with F. Chazal and D. Cohen-Steiner a method to approximate the curvature measures of a set with positive reach from a discrete approximation. This has been applied to the detection of sharp edges with M. Ovsjanikov and L. Guibas.

## Preprints

*Discretization of functionals involving the Monge-Ampère operator*

J.D. Benamou, G. Carlier, Q. Mérigot, É. Oudet*Discrete optimal transport: complexity, geometry and applications*

Q. Mérigot, É. Oudet

## Publications

*Handling convexity-like constraints in variational problems*

Q. Mérigot, É. Oudet

SIAM Journal on Numerical Analysis,**52**(5), 2466–2487, 2014.*Far-field reflector problem under design constraints*

J. André, D. Attali, Q. Mérigot, B. Thibert

International Journal of Computational Geometry and Applications (IJCGA), to appear.*Robust Geometry Estimation using the Generalized Voronoi Covariance Measure*

L. Cuel, J.O. Lachaud, Q. Mérigot, B. Thibert

SIAM Journal on Imaging Science (SIIMS), to appear*Intersection of paraboloids and application to Minkowski-type problems*

P. Machado Manhães de Castro, Q. Mérigot, B. Thibert

Proceedings of the 30th ACM Symposium on Computational Geometry, 2014*On the reconstruction of convex sets from random normals measurements*

H. Abdallah, Q. Mérigot

Discrete and Computational Geometry, to appear

Also in Proceedings of the 30th ACM Symposium on Computational Geometry, 2014*Lower bounds for k-distance approximation*.

Q. Mérigot

Proceedings of the 29th ACM Symposium on Computational Geometry, 2013*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).*Witnessed k-distance*.

L. Guibas, Q. Mérigot, D. Morozov

Discrete and Computational Geometry,**49**(1) 22–45, 2013 (also Proc SoCG 2011).*A multiscale approach to optimal transport*.

Q. Mérigot

Computer Graphics Forum**30**(5) 1583–1592, 2011 (also Proc SGP 2011).*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*Geometric inference for probability measures*.

F. Chazal, D. Cohen-Steiner, Q. Mérigot

Foundations of Computational Mathematics**11**, 733-751 (2011).*Boundary measures for geometric inference*.

F. Chazal, D. Cohen-Steiner, Q. Mérigot

Foundation of Computational Mathematics**10**, 221-240 (2010).*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).*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).*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.*Anosov AdS representations are quasi-Fuchsian*.

T. Barbot, Q. Mérigot

Groups, Geometry and Dynamics**6**(3), 441-483 (2012).

## Notes, thesis, surveys

*A comparison of two dual methods for discrete optimal transport*.

Geometric Science of Information, LNCS 8085, 389-396, 2013

Q. Mérigot*Geometric structure detection in point clouds*

Q.Mérigot

Thèse de doctorat, Université de Nice Sophia-Antipolis.