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Research Papers

Automatic Detection of Tangential Discontinuities in Point Cloud Data

[+] Author and Article Information
Hao Song

 Virtualwind Inc., Calgary, AB, T2P 1H4, Canada

Hsi-Yung Feng, Daoshan OuYang

Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada Husky Injection Molding Systems Ltd., Bolton, ON, L7E 5S5, Canada

J. Comput. Inf. Sci. Eng 8(2), 021001 (Apr 16, 2008) (10 pages) doi:10.1115/1.2904930 History: Received July 10, 2006; Revised January 01, 2008; Published April 16, 2008

A point cloud data set, a dense set of discrete coordinate points scanned or sampled from the surface of a 3D physical object or design model, is emerging as a new representation format for geometric modeling. This paper presents a new method to detect tangential discontinuities in point cloud data. The method introduces an original criterion, named as incompatibility, to quantify the magnitude of shape change in the vicinity of a data point. The introduced criterion is unique since in smooth regions of the underlying surface where shape change around a data point is small, the calculated incompatibilities tend to cluster around small values. At points close to tangential discontinuities, the calculated incompatibilities become relatively large. By modeling the incompatibilities of points in smooth regions following a statistical distribution, the proposed method identifies tangential discontinuities as those points whose incompatibilities are considered outliers with respect to the distribution. As the categorization of outliers is in effect independent of the underlying surface shape and sampling conditions of the data points, a threshold can be automatically determined via a generic procedure and used to identify tangential discontinuities. The effectiveness of the proposed method is demonstrated through many case studies using both simulated and practical point cloud data sets.

Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Tangent vector extrapolation

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Figure 2

Deviation of the extrapolated normal vectors

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Figure 3

Calculated incompatibilities for data sets sampled from cylindrical surfaces of different shapes: (a) noise free and (b) 10% noise

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Figure 4

Calculated incompatibilities for data sets sampled from cylindrical surfaces with different sampling densities: (a) noise free and (b) 10% noise

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Figure 5

Impact of measurement noise on the calculated incompatibilities

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Figure 6

Practical examples of the incompatibility histogram: (a) fan disk and (b) bunny

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Figure 7

Probability plot for outlier detection

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Figure 8

Identified points using (a) true and (b) estimated normal vectors

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Figure 9

Effect of parameter C in defining outliers on the number of identified points

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Figure 10

Cactus: identified points and the probability plot

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Figure 11

Fandisk: identified points and the probability plot

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Figure 12

Teapot: identified points and the probability plot

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Figure 13

Moeller: identified points and the probability plot

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Figure 14

Club: identified points and the probability plot

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Figure 15

Bunny: identified points and the probability plot

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