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Proceedings Paper

Geometric and topological feature extraction of linear segments from 2D cross-section data of 3D point clouds
Author(s): Rajesh Ramamurthy; Kevin Harding; Xiaoming Du; Vincent Lucas; Yi Liao; Ratnadeep Paul; Tao Jia
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Paper Abstract

Optical measurement techniques are often employed to digitally capture three dimensional shapes of components. The digital data density output from these probes range from a few discrete points to exceeding millions of points in the point cloud. The point cloud taken as a whole represents a discretized measurement of the actual 3D shape of the surface of the component inspected to the measurement resolution of the sensor. Embedded within the measurement are the various features of the part that make up its overall shape. Part designers are often interested in the feature information since those relate directly to part function and to the analytical models used to develop the part design. Furthermore, tolerances are added to these dimensional features, making their extraction a requirement for the manufacturing quality plan of the product. The task of “extracting” these design features from the point cloud is a post processing task. Due to measurement repeatability and cycle time requirements often automated feature extraction from measurement data is required. The presence of non-ideal features such as high frequency optical noise and surface roughness can significantly complicate this feature extraction process. This research describes a robust process for extracting linear and arc segments from general 2D point clouds, to a prescribed tolerance. The feature extraction process generates the topology, specifically the number of linear and arc segments, and the geometry equations of the linear and arc segments automatically from the input 2D point clouds. This general feature extraction methodology has been employed as an integral part of the automated post processing algorithms of 3D data of fine features.

Paper Details

Date Published: 19 May 2015
PDF: 11 pages
Proc. SPIE 9489, Dimensional Optical Metrology and Inspection for Practical Applications IV, 948905 (19 May 2015); doi: 10.1117/12.2179987
Show Author Affiliations
Rajesh Ramamurthy, GE Global Research (United States)
Kevin Harding, GE Global Research (United States)
Xiaoming Du, GE Global Research (China)
Vincent Lucas, GE Global Research (United States)
Yi Liao, GE Global Research (United States)
Ratnadeep Paul, GE Global Research (United States)
Tao Jia, GE Global Research (United States)

Published in SPIE Proceedings Vol. 9489:
Dimensional Optical Metrology and Inspection for Practical Applications IV
Kevin G. Harding; Toru Yoshizawa, Editor(s)

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