184 research outputs found
An algorithm for optimal transport between a simplex soup and a point cloud
We propose a numerical method to find the optimal transport map between a
measure supported on a lower-dimensional subset of R^d and a finitely supported
measure. More precisely, the source measure is assumed to be supported on a
simplex soup, i.e. on a union of simplices of arbitrary dimension between 2 and
d. As in [Aurenhammer, Hoffman, Aronov, Algorithmica 20 (1), 1998, 61--76] we
recast this optimal transport problem as the resolution of a non-linear system
where one wants to prescribe the quantity of mass in each cell of the so-called
Laguerre diagram. We prove the convergence with linear speed of a damped
Newton's algorithm to solve this non-linear system. The convergence relies on
two conditions: (i) a genericity condition on the point cloud with respect to
the simplex soup and (ii) a (strong) connectedness condition on the support of
the source measure defined on the simplex soup. Finally, we apply our algorithm
in R^3 to compute optimal transport plans between a measure supported on a
triangulation and a discrete measure. We also detail some applications such as
optimal quantization of a probability density over a surface, remeshing or
rigid point set registration on a mesh
Computing weak distances between the 2-sphere and its nonsmooth approximations
A novel algorithm is proposed for quantitative comparisons between compact
surfaces embedded in the three-dimensional Euclidian space. The key idea is to
identify those objects with the associated surface measures and compute
distances between them using the Fourier transform on the ambient space. In
particular, the inhomogeneous Sobolev norms of negative order are approximated
from data in the frequency space, which amounts to comparing measures after
appropriate smoothing. Such Fourier-based distances allow several advantages
including high accuracy due to fast-converging numerical quadrature rules,
acceleration by the nonuniform fast Fourier transform, parallelization on
massively parallel architectures. In numerical experiments, the 2-sphere, which
is an example whose Fourier transform is explicitly known, is compared with its
icosahedral discretization, and it is observed that the piecewise linear
approximations converge to the smooth object at the quadratic rate up to small
truncations.Comment: 14 pages, 4 figure
Dynamical Optimal Transport on Discrete Surfaces
We propose a technique for interpolating between probability distributions on
discrete surfaces, based on the theory of optimal transport. Unlike previous
attempts that use linear programming, our method is based on a dynamical
formulation of quadratic optimal transport proposed for flat domains by Benamou
and Brenier [2000], adapted to discrete surfaces. Our structure-preserving
construction yields a Riemannian metric on the (finite-dimensional) space of
probability distributions on a discrete surface, which translates the so-called
Otto calculus to discrete language. From a practical perspective, our technique
provides a smooth interpolation between distributions on discrete surfaces with
less diffusion than state-of-the-art algorithms involving entropic
regularization. Beyond interpolation, we show how our discrete notion of
optimal transport extends to other tasks, such as distribution-valued Dirichlet
problems and time integration of gradient flows
Dynamical optimal transport on discrete surfaces
We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport. Unlike previous attempts that use linear programming, our method is based on a dynamical formulation of quadratic optimal transport proposed for flat domains by Benamou and Brenier [2000], adapted to discrete surfaces. Our structure-preserving construction yields a Riemannian metric on the (finitedimensional) space of probability distributions on a discrete surface, which translates the so-called Otto calculus to discrete language. From a practical perspective, our technique provides a smooth interpolation between
distributions on discrete surfaces with less diffusion than state-of-the-art algorithms involving entropic regularization. Beyond interpolation, we show how our discrete notion of optimal transport extends to other tasks, such as distribution-valued Dirichlet problems and time integration of gradient flows
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