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Proceedings Paper

Aspects of 3D shape reconstruction
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Paper Abstract

The ability to reconstruct the three dimensional (3D) shape of an object from multiple images of that object is an important step in certain computer vision and object recognition tasks. The images in question can range from 2D optical images to 1D radar range profiles. In each case, the goal is to use the information (primarily invariant geometric information) contained in several images to reconstruct the 3D data. In this paper we apply a blend of geometric, computational, and statistical techniques to reconstruct the 3D geometry, specifically the shape, from multiple images of an object. Specifically, we deal with a collection of feature points that have been tracked from image (or range profile) to image (or range profile) and we reconstruct the 3D point cloud up to certain transformations-affine transformations in the case of our optical sensor and rigid motions (translations and rotations) in the radar case. Our paper discusses the theory behind the method, outlines the computational algorithm, and illustrates the reconstruction for some simple examples.

Paper Details

Date Published: 2 February 2009
PDF: 12 pages
Proc. SPIE 7246, Computational Imaging VII, 72460Q (2 February 2009); doi: 10.1117/12.807736
Show Author Affiliations
Peter F. Stiller, Texas A&M Univ. (United States)
Gregory Arnold, Air Force Research Lab. (United States)
Matthew Ferrara, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 7246:
Computational Imaging VII
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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