Surface reconstruction method using deformable templates

Yuan-Fang Wang; Jih-Fang Wang

doi:10.1117/12.25263

1 April 1991 Surface reconstruction method using deformable templates

Yuan-Fang Wang, Jih-Fang Wang

Proceedings Volume 1383, Sensor Fusion III: 3D Perception and Recognition; (1991) https://doi.org/10.1117/12.25263
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

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.

Citation Download Citation

Yuan-Fang Wang and Jih-Fang Wang "Surface reconstruction method using deformable templates", Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); https://doi.org/10.1117/12.25263

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KEYWORDS

Fusion energy

3D modeling

3D image processing

3D image reconstruction

Image sensors

Sensor fusion

Image fusion

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