Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Computing and simplifying 2D and 3D continuous skeletons
Computer Vision and Image Understanding
The crust and the &Bgr;-Skeleton: combinatorial curve reconstruction
Graphical Models and Image Processing
r-regular shape reconstruction from unorganized points
Computational Geometry: Theory and Applications - special issue on applied computational geometry
Reconstruction curves with sharp corners
Proceedings of the sixteenth annual symposium on Computational geometry
Proceedings of the sixth ACM symposium on Solid modeling and applications
Digital Image Processing
Feature-Preserving Medial Axis Noise Removal
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Graphical Models
A Mathematical Tool to Extend 2D Spatial Operations to Higher Dimensions
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Curve reconstruction from noisy samples
Computational Geometry: Theory and Applications - Special issue on the 19th annual symposium on computational geometry - SoCG 2003
A simplex-based approach to implement dimension independent spatial analyses
Computers & Geosciences
Watershed delineation from the medial axis of river networks
Computers & Geosciences
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Continuous curves are approximated by sampling. If sampling is sufficiently dense, the sample points carry the shape information of the curve and so can be used to reconstruct the original curve. There have been lots of efforts to reconstruct curves from sample points. This paper reviews the curve reconstruction methods that use Voronoi diagram in their approach. We, then, describe the main issues of these methods and suggest solutions to deal with them. Especially, we improve one of the Voronoi-based curve reconstruction algorithms (called one-step crust algorithm) by labeling the sample points as a pre-processing. The highlights of our proposed approach are (1) It is simple and easy to implement; (2) It is robust to boundary perturbations and noises; (3) Special cases in sampling like sharp corners can be handled; and (4) It can be used for reconstructing open curves.