Recovery of 3-D closed surfaces using progressive shell models

Remin Lin, Wei Chung Lin, Chin Tu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper is concerned with the problems of reconstructing a closed surface from scattered, noisy 3-D data. A progressive shell model is a 3-D extension of the 2-D progressive contour model. We employ finite element methods (FEMs) to reduce the number of the required variables and improve the efficiency in storage and computation. The fundamental forms in differential geometry are used to measure rigid-motion invariant properties and formulate the internal energy of the shell. We also develop a wireframe model associated with a subdivision scheme to overcome the difficulty of generating a smooth boundary between two adjacent patches. This is a direct application of the 2-D contour model where curve segments or wires are used instead of patches. In the subdivision scheme, we impose the co-plane constraints to determine a unique normal vector at the interpolated mid-point. To demonstrate the descriptive ability of a wireframe model, we conduct experiments on 3-D data set of a tumor and a face.

Original languageEnglish (US)
Title of host publicationTrack A
Subtitle of host publicationComputer Vision
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages95-99
Number of pages5
ISBN (Print)081867282X, 9780818672828
DOIs
StatePublished - Jan 1 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: Aug 25 1996Aug 29 1996

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Other

Other13th International Conference on Pattern Recognition, ICPR 1996
CountryAustria
CityVienna
Period8/25/968/29/96

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Lin, R., Lin, W. C., & Chen, C. T. (1996). Recovery of 3-D closed surfaces using progressive shell models. In Track A: Computer Vision (pp. 95-99). [545998] (Proceedings - International Conference on Pattern Recognition; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.1996.545998