Fitting 3D curves to unorganized data points using deformable curves
CG International '92 Proceedings of the 10th International Conference of the Computer Graphics Society on Visual computing : integrating computer graphics with computer vision: integrating computer graphics with computer vision
Implicit Simplicial Models for Adaptive Curve Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The crust and the &Bgr;-Skeleton: combinatorial curve reconstruction
Graphical Models and Image Processing
The approximation power of moving least-squares
Mathematics of Computation
A simple provable algorithm for curve reconstruction
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Curve reconstruction from unorganized points
Computer Aided Geometric Design
Curve reconstruction: connecting dots with good reason
Computational Geometry: Theory and Applications
Grouping and parameterizing irregularly spaced points for curve fitting
ACM Transactions on Graphics (TOG)
Reconstructing a collection of curves with corners and endpoints
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Traveling Salesman-Based Curve Reconstruction in Polynomial Time
SIAM Journal on Computing
Provable surface reconstruction from noisy samples
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
How to cover a point set with a V-shape of minimum width
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
Voronoi-Based curve reconstruction: issues and solutions
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
How to cover a point set with a V-shape of minimum width
Computational Geometry: Theory and Applications
Watershed delineation from the medial axis of river networks
Computers & Geosciences
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We present an algorithm to reconstruct a collection of disjoint smooth closed curves from noisy samples. Our noise model assumes that the samples are obtained by first drawing points on the curves according to a locally uniform distribution followed by a uniform perturbation in the normal directions. Our reconstruction is faithful with probability approaching 1 as the sampling density increases.