Curve Reconstruction Algorithm Based on Discrete Data Points and Normal Vectors
Received:June 17, 2019  Revised:August 25, 2019
Key Word: Curve reconstruction   Curve fitting   Normal vector   B-spline  
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Author NameAffiliationE-mail
ming-yang guo dalian university of technology 
chong-jun li dalian university of technology  
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      This paper presents a curve reconstruction algorithm based on discrete data and normal vectors using B-splines. By adding normal constraints, this method can keep good fitting results when the data noise is big. This method consists of four steps: discrete points parameterization, B-spline dominant point selection, knot vector determination and curve fitting method based on discrete data points and normal vectors. The proposed algorithm has been improved in: parameterization of the discrete data points, the selection of dominant points of the B-spline, and the determination of the weight to balancing the errors between the data points and normal vectors. Therefore, we transform the B-spline fitting problem into three sub-problems. By obtaining more precise parameterization, arranging more knots appropriately in the complex regions, and adding normal regularization, this approach can obtain B-spline curve adaptively. Compared with other approaches, the B-spline curve reconstructed by our approach can retain better geometric features of the original curve when the given data set contains high strength noise.
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