Robust reconstruction of 2D curves from scattered noisy point data

  • Authors:
  • Jun Wang;Zeyun Yu;Weizhong Zhang;Mingqiang Wei;Changbai Tan;Ning Dai;Xi Zhang

  • Affiliations:
  • -;-;-;-;-;-;-

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2014

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Abstract

In this paper, a robust algorithm is proposed for reconstructing 2D curve from unorganized point data with a high level of noise and outliers. By constructing the quadtree of the input point data, we extract the ''grid-like'' boundaries of the quadtree, and smooth the boundaries using a modified Laplacian method. The skeleton of the smoothed boundaries is computed and thereby the initial curve is generated by circular neighboring projection. Subsequently, a normal-based processing method is applied to the initial curve to smooth jagged features at low curvatures areas, and recover sharp features at high curvature areas. As a result, the curve is reconstructed accurately with small details and sharp features well preserved. A variety of experimental results demonstrate the effectiveness and robustness of our method.