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Proceedings Paper

Automatic reconstruction of large 3D models of real environments from unregistered data-sets
Author(s): Faysal Boughorbal; David L. Page; Mongi A. Abidi
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Paper Abstract

Towards photo-realistic 3D scene reconstruction form range and color images, we present a statistical technique for multi-modal image registration. Statistical tools are employed to measure the dependence of tow imags, considered as random distributions of pixels, and to find the pose of one imaging system relative to the other. The similarity metrics used in our automatic registration algorithm are based on the chi-squared measure of dependence, which is presented as an alternative to the standard mutual information criterion. These two criteria belong to the class of information-theoretic similarity measures that quantify the dependence in terms of information provided by one image about the other. This approach requires the use of a robust optimization scheme for the maximization of the similarity measure. To achieve accurate reslut, we investigated the use of heuristics such as genetic algorithms. The retrieved pose parameters are used to generate a texture map from the color image, and the occluded areas in this image are determined and labeled. Finally the 3D scene is rendered as a triangular mesh with texture.

Paper Details

Date Published: 16 March 2000
PDF: 10 pages
Proc. SPIE 3958, Three-Dimensional Image Capture and Applications III, (16 March 2000); doi: 10.1117/12.380047
Show Author Affiliations
Faysal Boughorbal, Ecole Nationale des Ingenieurs de Tunis (Tunisia) and Univ. of Tennessee/Knoxville (United States)
David L. Page, Univ. of Tennessee/Knoxville (United States)
Mongi A. Abidi, Univ. of Tennessee/Knoxville (United States)

Published in SPIE Proceedings Vol. 3958:
Three-Dimensional Image Capture and Applications III
Brian D. Corner; Joseph H. Nurre, Editor(s)

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