SURFACE RECONSTRUCTION IN COMPUTER VISION.

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University of MICHIGAN, 1991 - Computer vision - 153 pages
This thesis concentrates on a two-stage algorithm for surface reconstruction from sparse data. We present methods to handle noise, outliers and discontinuities in a common framework. The basic paradigm is to clean and grid (the first stage), and then to fit the data with a discontinuity preserving spline (the second stage). The first stage consists of a robust local approximation algorithm to both remove outliers in the data and create a grid from the original scattered data points which preserves

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Contents

BACKGROUND
18
THEORY AND ALGORITHMS FOR SURFACE RECONSTRUC
43
EXPERIMENTS
63
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