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Robust Recovery of 3D Geometric Primitives From Point Cloud

[+] Author Affiliations
Xiang Yang, Peter Meer, Hae Chang Gea

Rutgers, State University of New Jersey, Piscataway, NJ

Paper No. DETC2017-67564, pp. V001T02A009; 10 pages
doi:10.1115/DETC2017-67564
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 37th Computers and Information in Engineering Conference
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5811-0
  • Copyright © 2017 by ASME

abstract

A robust method for surface fitting in 3D point cloud is presented as an application of the robust estimation of multiple in-lier structures algorithm [1]. The geometric primitives such as planes, spheres and cylinders are detected from the point samples in the noisy dataset, without regenerating surface normals or mesh. The inlier points of different surfaces are classified and segmented, with the tolerance of error for each surface estimated adaptively from the input data. From the segmented points, designers can interact with the geometric primitives conveniently. Direct modification of 3D point cloud and inverse design of solid model can be applied. Both synthetic and real point cloud datasets are tested for the use of the robust algorithm.

Copyright © 2017 by ASME

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