Automatic reconstruction of large 3D models of real environments from unregistered data-sets

Faysal Boughorbal; David L. Page; Mongi A. Abidi

doi:10.1117/12.380047

16 March 2000 Automatic reconstruction of large 3D models of real environments from unregistered data-sets

Faysal Boughorbal, David L. Page, Mongi A. Abidi

Proceedings Volume 3958, Three-Dimensional Image Capture and Applications III; (2000) https://doi.org/10.1117/12.380047
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

Faysal Boughorbal, David L. Page, and Mongi A. Abidi "Automatic reconstruction of large 3D models of real environments from unregistered data-sets", Proc. SPIE 3958, Three-Dimensional Image Capture and Applications III, (16 March 2000); https://doi.org/10.1117/12.380047

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