David Coeurjolly

David Coeurjolly

Directeur de recherche

CNRS

LIRIS

Université de Lyon

Biography

I am Directeur de Recherche CNRS at LIRIS in the Origami team. I am also Director of the Fédération Informatique de Lyon (CNRS, FR2000). My main focus is digital geometry, geometry processing and point sampling for Monte Carlo rendering.

Interests

  • Discrete Geometry
  • Geometry Processing
  • Point sampling in computer graphics

Education

  • PhD in Computer Science, 2002

    Université Lumière Lyon2, France

  • M.Sc. in Computer Science, 2009

    École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, France

  • BSc in Computer Science, 2008

    École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, France

Projects

ANR ROOT (2016-2020)

ROOT is a project funded by the ANR throught the young researchers program JCJC. ROOT aims at developping numerical methods for solving regression problems involving optimal transport, with applications in computer graphics and vision. These problems include data fitting, supervised or unsupervised learning, or statistical inference, applied to histogram features (P.I. Nicolas Bonneel).

ANR COMEDIC (2015-2020)

The project CoMeDiC is funded by the ANR. CoMeDiC stands for Convergent Metrics for Digital Calculus. (P.I: Jacques-Olivier Lachaud)

ANR PARADIS (2018-2022)

PARameter-free Analysis of DIgital Surfaces (P.I: Tristan Roussillon)

Former Projects

ANR digitalSnow (2011-2016)

The main purpose of this project is to provide efficient computational tools to study the snow metamorphism from 3D images of real snow microstructures acquired using X-ray tomography techniques. (P.I. David Coeurjolly)

Recent & Upcoming Talks

Geometry Processing on Voxel Data

Geometry Processing on Voxel Data

Geometry Processing on Voxel Data

Digital Geometry in Computer Graphics

Échantillonnage basse discrépance et bruit bleu en informatique graphique

Recent Publications

Quickly discover relevant content by filtering publications.

Ground Metric Learning on Graphs

Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between …

Interpolated corrected curvature measures for polygonal surfaces

A consistent and yet practically accurate definition of curvature onto polyhedral meshes remains an open problem. We propose a new …

Code Replicability in Computer Graphics

Being able to duplicate published research results is an important process of conducting research whether to build upon these findings …

Sliced Optimal Transport Sampling

In this paper, we introduce a numerical technique to generate sample dis- tributions in arbitrary dimension for improved accuracy of …

Robust Normal Vector Estimation in 3D Point Clouds through Iterative Principal Component Analysis

This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our …

Code

Please refer to the project webpages for licensing and copyrights. More projects on my Github webpage. Some codes are also available with my publications.

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Code Replicablity

Code replicablity in computer graphics

DGtal

Digital Geometry Tools and Algorithm library

uni{form|corn} toolkit

Sampling patterns in a numerical integration using Monte-Carlo estimators.

OT Color Transfer

Color Transfer via Sliced Optimal Transport

VolGallery

A gallery of volumetric images and objects

LDBN

Low Discrepancy Blue Noise sampler

Poincaré Disk

Drawing in the Poincaré disk model (one of the n-d hyperbolic geometry models)

ICTV

Interactive Curvature Tensor Visualization

SPOT

Sliced Partial Optimal Transport

Contact