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Proceedings Paper

Adaptive surface reconstruction
Author(s): Demetri Terzopoulos; Manuela Vasilescu
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Paper Abstract

This paper introduces a new approach to surface reconstruction motivated by concepts from numerical grid generation. We develop adaptive mesh models that nonuniformly sample and reconstruct input shape data. Adaptive meshes are dynamic models assembled from nodal masses connected by adjustable springs. Acting as mobile sampling sites, the nodes observe interesting properties of the input data, such as depths, gradients, and curvatures. The springs automatically adjust their stiffnesses based on the locally sampled information in order to concentrate nodes near rapid shape variations. The representational power of an adaptive mesh is enhanced by its ability to optimally distribute the available degrees of freedom in accordance with the local complexity of the input data. Surface reconstruction using adaptive meshes runs at interactive rates with continuous 3D display on a graphics workstation.

Paper Details

Date Published: 1 April 1991
PDF: 8 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25262
Show Author Affiliations
Demetri Terzopoulos, Univ. of Toronto and Schlumberger Lab. for Computer Science (Canada)
Manuela Vasilescu, MIT and Schlumberger Lab. for Computer Science (United States)

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

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