Optimal Transport Approximation of 2-Dimensional Measures - Archive ouverte HAL Access content directly
Journal Articles SIAM Journal on Imaging Sciences Year : 2019

Optimal Transport Approximation of 2-Dimensional Measures


We propose a fast and scalable algorithm to project a given density on a set of structured measures defined over a compact 2D domain. The measures can be discrete or supported on curves for instance. The proposed principle and algorithm are a natural generalization of previous results revolving around the generation of blue-noise point distributions, such as Lloyd's algorithm or more advanced techniques based on power diagrams. We analyze the convergence properties and propose new approaches to accelerate the generation of point distributions. We also design new algorithms to project curves onto spaces of curves with bounded length and curvature or speed and acceleration. We illustrate the algorithm's interest through applications in advanced sampling theory, non-photorealistic rendering and path planning.
Fichier principal
Vignette du fichier
Optimal Transport Approximation of 2-Dimensional Measures.pdf (10.31 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01773993 , version 1 (23-04-2018)
hal-01773993 , version 2 (05-07-2019)


  • HAL Id : hal-01773993 , version 2


Frédéric de Gournay, Jonas Kahn, Léo Lebrat, Pierre Weiss. Optimal Transport Approximation of 2-Dimensional Measures. SIAM Journal on Imaging Sciences, 2019. ⟨hal-01773993v2⟩
508 View
474 Download


Gmail Facebook X LinkedIn More