Hostname: page-component-77c89778f8-sh8wx Total loading time: 0 Render date: 2024-07-20T04:25:27.675Z Has data issue: false hasContentIssue false
  • English
  • Français

Wire-frame modelling of polyhedral objects from rangefinder data

Published online by Cambridge University Press:  09 March 2009

J. M. Badcock  and
R. A. Jarvist
J. M. Badcock
Affiliation:
Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria 3052 (Australia).
R. A. Jarvist
Affiliation:
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria 3168 (Australia).
Get access Rights & Permissions [Opens in a new window]

Extract

Methods are described for computer analysis of image-data from a coded-stripe rangefinder. The main objective is to find vertex coordinates and connectivity information for a polyhedral object, enabling it to be represented by a wire-frame model. For each of several rangefinder viewpoints, the data is processed to extract three-dimensional edge and vertex positions. The emphasis is on estimation techniques that make good use of fairly sparse data-points. Results from different viewpoints are merged to produce a 3D model of the object.

Type
Research Article
Information
Robotica , Volume 12 , Issue 1 , January 1994 , pp. 65 - 75
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Tu, X.-W. and Dubuisson, B., “3-D information derivation from a pair of binocular images” Pattern Recognition 23, No. 3/4, 223–235 (1992).CrossRefGoogle Scholar
2.Oshima, M. and Shirai, Y., “Object recognition using three-dimensional information” IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-5, No. 4, 353–361 (07, 1983).CrossRefGoogle ScholarPubMed
3.Bolle, R. M. and Cooper, D. B., “On optimally combining pieces of information, with application to estimating 3-D complex-object position from range data” IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-8, No. 5, 619–638 (10., 1986).CrossRefGoogle ScholarPubMed
4.Hoffmann, R. and Jain, A. K., “Segmentation and classification of range images”, IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-9, No. 5, 608–620 (10., 1987).CrossRefGoogle Scholar
5.Nagata, T. and Zha, H. B., “Determining orientation, location and size of primitive surfaces by a modified Hough transformation technique” Pattern Recognition 21, No. 5, 481–491 (1988).CrossRefGoogle Scholar
6.Grossmann, P., “From 3D line segments to objects and spaces” Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 06 4–8, 1989 (IEEE Computer Society Press, Washington, DC, 1989) pp. 216–221.Google Scholar
7.Duda, R.O., Nitzan, D. and Barrett, P., “Use of range and reflectance data to find planar surface regions” IEEE Trans. on Pattern Analysis and Machine intelligence PAMI-1, No. 3, 259–271 (07, 1979).CrossRefGoogle ScholarPubMed
8.Faugeras, O.D. and Hebert, M., “A 3-D recognition and positioning algorithm using geometrical matching between primitive surfaces” IJCAI-83, Proceedings of the Eighth international Joint Conferende on Artificial Intelligence, 8–1208 1983, Karlsruhe, West Germany (1983) pp. 996–1002.Google Scholar
9.Dhome, M. and Kasvand, T., “Polyhedra recognition by hypothesis accumulation” IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-9, No. 3, 429–438 (05, 1987).CrossRefGoogle ScholarPubMed
10.Taylor, R.W., Savini, M. and Reeves, A.P., “Fast segmentation of range imagery into planar regions” Computer Vision, Graphics and Image Processing 45, 42–60 (1989).CrossRefGoogle Scholar
11.Mukherjee, J., Das, P.P. and Chatterji, B.N., “An algorithm for the extraction of the wire frame structure of a three-dimensional object” Pattern Recognition 23, No. 9, 999–1010 (1990).CrossRefGoogle Scholar
12.Kishnapuram, R. and Freg, C.P., “Fitting an unknown number of lines and planes to image data through compatible cluster merging” Pattern Recognition 25, No. 4, 385–400 (1992).CrossRefGoogle Scholar
13.Inokuchi, S. and Nevatia, R., “Boundary detection in range pictures” Proc. 5th Int. Joint Conf. on Pattern Recognition, Miami Beach, Florida (12., 1980) pp. 1301–1303.Google Scholar
14.Koivunen, V., Silven, O. and Pietikainen, M., “Edge detection in range images” In: (Simon, J. C., Ed., From Pixels to Features, Proceedings of a Workshop held at Bonas, France, 22–2708, 1988 (North-Holland Publishing Co., Amsterdam, 1989) pp. 175–184.Google Scholar
15.Alexander, B.F., “High Accuracy Non-Contact Three Dimensional Shape Measurement’. PhD thesis (Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia, 1989).Google Scholar
16.Thomas, S.M. and Chan, Y.T., “A simple approach for the estimation of circular arc center and its radius” Computer Vision, Graphics and Image Processing 45, No. 3, 362–370 (03, 1989).CrossRefGoogle Scholar
17.Pearson, K., “On lines and planes of closest fit to systems of points in space” Phil. Mag., Series 6, Vol. 2, No. 11, 559–572 (11., 1901).CrossRefGoogle Scholar
18.Brent, R.P., Algorithms for Minimization Without Derivatives (Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1973).Google Scholar