Add to ORKGWe present an algorithm to "reconstruct" a smooth k-dimensional manifold M embedded in an Euclidean space ℝ<sup>d</sup> from a "sufficiently dense" point sample from the manifold. The algorithm outputs a simplicial manifold that is homeomorphic and geometrically close to M. The running time is O(n log n) where n is the number of points in the sample (the multiplicative constant depends exponentially on the dimension though)
In this paper, we consider the Précis problem of sampling K representative yet diverse data points ...
Manifold reconstruction has been extensively studied for the last decade or so, especially in two an...
Known algorithms for reconstructing a 2-manifold from a point sample in R3 are naturally based on de...
A new algorithm for manifold reconstruction is presented. The goal is to take samples drawn from a f...
We give a provably correct algorithm to reconstruct a k-dimensional manifold embedded in d-dimension...
Let P be a dense set of points sampled from an Tridimensional compact smooth manifold ∑ in Rd. We sh...
In this thesis we address some of the problems in the field of piecewise linear approxima- tion of k...
We describe and demonstrate an algorithm that takes as input an unorganized set of points {x1, ..., ...
From a sufficiently large point sample lying on a compact Riemannian submanifold of Euclidean space,...
In recent years, manifold learning has become increasingly popular as a tool for performing non-line...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
Known algorithms for reconstructing a 2-manifold from a point sample in R3 are naturally based on de...
It is a well-established fact that the witness complex is closely related to the restricted Delaunay...
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifol...
It is one of the main goals of Computer Graphics in particular, and science in general, to understan...
In this paper, we consider the Précis problem of sampling K representative yet diverse data points ...
Manifold reconstruction has been extensively studied for the last decade or so, especially in two an...
Known algorithms for reconstructing a 2-manifold from a point sample in R3 are naturally based on de...
A new algorithm for manifold reconstruction is presented. The goal is to take samples drawn from a f...
We give a provably correct algorithm to reconstruct a k-dimensional manifold embedded in d-dimension...
Let P be a dense set of points sampled from an Tridimensional compact smooth manifold ∑ in Rd. We sh...
In this thesis we address some of the problems in the field of piecewise linear approxima- tion of k...
We describe and demonstrate an algorithm that takes as input an unorganized set of points {x1, ..., ...
From a sufficiently large point sample lying on a compact Riemannian submanifold of Euclidean space,...
In recent years, manifold learning has become increasingly popular as a tool for performing non-line...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
Known algorithms for reconstructing a 2-manifold from a point sample in R3 are naturally based on de...
It is a well-established fact that the witness complex is closely related to the restricted Delaunay...
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifol...
It is one of the main goals of Computer Graphics in particular, and science in general, to understan...
In this paper, we consider the Précis problem of sampling K representative yet diverse data points ...
Manifold reconstruction has been extensively studied for the last decade or so, especially in two an...
Known algorithms for reconstructing a 2-manifold from a point sample in R3 are naturally based on de...