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Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations
October 2009 (vol. 31 no. 10)
pp. 1906-1912
Matthew Ferrara, Air Force Research Laboratory, Dayton
Gregory Arnold, Air Force Research Laboratory, Dayton
Mark Stuff, Michigan Tech Research Institute, Ann Arbor
This paper describes an invariant-based shape- and motion reconstruction algorithm for 3D-to-1D orthographically projected range data taken from unknown viewpoints. The algorithm exploits the object-image relation that arises in echo-based range data and represents a simplification and unification of previous work in the literature. Unlike one proposed approach, this method does not require uniqueness constraints, which makes its algorithmic form independent of the translation removal process (centroid removal, range alignment, etc.). The new algorithm, which simultaneously incorporates every projection and does not use an initialization in the optimization process, requires fewer calculations and is more straightforward than the previous approach. Additionally, the new algorithm is shown to be the natural extension of the approach developed by Tomasi and Kanade for 3D-to-2D orthographically projected data and is applied to a realistic inverse synthetic aperture radar imaging scenario, as well as experiments with varying amounts of aperture diversity and noise.

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Index Terms:
Geometric invariants, object-image relation, factorization method, shape from motion, orthographic projection, moving-target imaging.
Matthew Ferrara, Gregory Arnold, Mark Stuff, "Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1906-1912, Oct. 2009, doi:10.1109/TPAMI.2008.294
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