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Proceedings Paper

Surface reconstruction method using deformable templates
Author(s): Yuan-Fang Wang; Jih-Fang Wang
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Paper Abstract

In this paper, we propose a unification framework for three-dimensional shape reconstruction using physically- based models. Most shape-from-X techniques use an “observable” (e.g., disparity, intensity, and texture gradient) and a model, which is based on specific domain knowledge (e.g., triangulation principle, reflectance function, and texture distortion equation) to predict the observable, in 3-D shape reconstruction. We show that all these “observable—prediction-model” types of techniques can be incorporated into our framework of energy constraint on a flexible, deformable image frame. In our algorithm, if the observable does not confirm with that predicted by the corresponding model, a large “error” potential results. The error potential gradient forces the flexible image frame to deform in space. The deformation brings the flexible image frame to “wrap” onto the surface of the imaged 3-D object. Surface reconstruction is thus achieved through a “package wrapping” process by minimizing the discrepancy in the observable and the model prediction. The dynamics of such a wrapping process are governed by the least action principle which is physically correct. A physically-based model is essential in this general shape reconstruction framework because of its capability to recover the desired 3-D shape, to provide an animation sequence of the reconstruction, and to include the regularization principle into the theory.

Paper Details

Date Published: 1 April 1991
PDF: 12 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25263
Show Author Affiliations
Yuan-Fang Wang, Univ. of California/Santa Barbara (United States)
Jih-Fang Wang, IBM Corp. (United States)

Published in SPIE Proceedings Vol. 1383:
Sensor Fusion III: 3D Perception and Recognition
Paul S. Schenker, Editor(s)

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