AbstractGiven a smooth compact codimension one submanifold S of Rk and a compact approximation K of S, we prove that it is possible to reconstruct S and to approximate the medial axis of S with topological guarantees using unions of balls centered on K. We consider two notions of noisy-approximation that generalize sampling conditions introduced by Amenta et al. and Dey et al. The first one generalizes uniform sampling based on the minimum value of the local feature size. The second one generalizes non-uniform sampling based on the local feature size function of S. The density and noise of the approximation are bounded by a constant times the local feature size function. This constant does not depend on the surface S. Our results are based ...
In this thesis we address some of the problems in the field of piecewise linear approxima- tion of k...
We introduce a parameterized notion of feature size that in-terpolates between the minimum of the lo...
The hypothesis that high dimensional data tends to lie in the vicinity of a low di-mensional manifol...
International audienceGiven a smooth compact codimension one submanifold S of Rk and a compact appro...
AbstractGiven a smooth compact codimension one submanifold S of Rk and a compact approximation K of ...
Volume-based boundary reconstruction processes often have to deal with non-manifold shapes. Even tho...
From a sufficiently large point sample lying on a compact Riemannian submanifold of Euclidean space,...
Volume based digitization processes often deal with non-manifold shapes. Even though many reconstruc...
International audienceWe introduce a parameterized notion of feature size that interpolates between ...
We present an algorithm that approximates the medial axis of a smooth manifold in $\mathbb{R}^3$ whi...
We present an algorithm for approximating a function defined over a d-dimensional manifold utilizing...
The distance function induced by a surface in R^n is known to carry a great deal of topological info...
Let P be a dense set of points sampled from an Tridimensional compact smooth manifold ∑ in Rd. We sh...
It is a well-established fact that the witness complex is closely related to the restricted Delaunay...
In this thesis, we look for methods for reconstructing an approximation of a manifold known only thr...
In this thesis we address some of the problems in the field of piecewise linear approxima- tion of k...
We introduce a parameterized notion of feature size that in-terpolates between the minimum of the lo...
The hypothesis that high dimensional data tends to lie in the vicinity of a low di-mensional manifol...
International audienceGiven a smooth compact codimension one submanifold S of Rk and a compact appro...
AbstractGiven a smooth compact codimension one submanifold S of Rk and a compact approximation K of ...
Volume-based boundary reconstruction processes often have to deal with non-manifold shapes. Even tho...
From a sufficiently large point sample lying on a compact Riemannian submanifold of Euclidean space,...
Volume based digitization processes often deal with non-manifold shapes. Even though many reconstruc...
International audienceWe introduce a parameterized notion of feature size that interpolates between ...
We present an algorithm that approximates the medial axis of a smooth manifold in $\mathbb{R}^3$ whi...
We present an algorithm for approximating a function defined over a d-dimensional manifold utilizing...
The distance function induced by a surface in R^n is known to carry a great deal of topological info...
Let P be a dense set of points sampled from an Tridimensional compact smooth manifold ∑ in Rd. We sh...
It is a well-established fact that the witness complex is closely related to the restricted Delaunay...
In this thesis, we look for methods for reconstructing an approximation of a manifold known only thr...
In this thesis we address some of the problems in the field of piecewise linear approxima- tion of k...
We introduce a parameterized notion of feature size that in-terpolates between the minimum of the lo...
The hypothesis that high dimensional data tends to lie in the vicinity of a low di-mensional manifol...