Robust smooth feature extraction from point clouds

Joel Daniels, Linh K. Ha, Tilo Ochotta, Cláudio T. Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Defining sharp features in a given 3D model facilitates a better understanding of the surface and aids visualizations, reverse engineering, filtering, simplification, non-photo realism, reconstruction and other geometric processing applications. We present a robust method that identifies sharp features in a point cloud by returning a set of smooth curves aligned along the edges. Our feature extraction is a multi-step refinement method that leverages the concept of Robust Moving Least Squares to locally fit surfaces to potential features. Using Newton's method, we project points to the intersections of multiple surfaces then grow polylines through the projected cloud. After resolving gaps, connecting corners, and relaxing the results, the algorithm returns a set of complete and smooth curves that define the features. We demonstrate the benefits of our method with two applications: surface meshing and point-based geometry compression.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Shape Modeling and Applications 2007, SMI'07
Pages123-133
Number of pages11
DOIs
StatePublished - Dec 1 2007
EventIEEE International Conference on Shape Modeling and Applications 2007, SMI'07 - Lyon, France
Duration: Jun 13 2007Jun 15 2007

Publication series

NameProceedings - IEEE International Conference on Shape Modeling and Applications 2007, SMI'07

Other

OtherIEEE International Conference on Shape Modeling and Applications 2007, SMI'07
CountryFrance
CityLyon
Period6/13/076/15/07

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ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Modeling and Simulation

Cite this

Daniels, J., Ha, L. K., Ochotta, T., & Silva, C. T. (2007). Robust smooth feature extraction from point clouds. In Proceedings - IEEE International Conference on Shape Modeling and Applications 2007, SMI'07 (pp. 123-133). [4273375] (Proceedings - IEEE International Conference on Shape Modeling and Applications 2007, SMI'07). https://doi.org/10.1109/SMI.2007.32