Extracting lines of curvature from noisy point clouds

  • Authors:

  • Evangelos Kalogerakis;Derek Nowrouzezahrai;Patricio Simari;Karan Singh

  • Affiliations:

  • DGP Lab, Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada M5S 3G4;DGP Lab, Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada M5S 3G4;DGP Lab, Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada M5S 3G4;DGP Lab, Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada M5S 3G4

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2009

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Abstract

We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.