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N. Gelfand, N. Mitra, L. Guibas and H. Pottmann. Robust Global Registration. Proc. Eurographics Symp. Geom. Processing, pp. 197-206, 2005.
Abstract:
We present an algorithm for the automatic alignment of two 3D shapes
(data and model), without any assumptions about their initial
positions. The algorithm computes for each surface point a descriptor
based on local geometry that is robust to noise. A small number of
feature points are automatically picked from the data shape according
to the uniqueness of the descriptor value at the point. For each
feature point on the data, we use the descriptor values of the model
to find potential corresponding points. We then develop a fast
branch-and-bound algorithm based on distance matrix comparisons to
select the optimal correspondence set and bring the two shapes into a
coarse alignment. The result of our alignment algorithm is used as the
initialization to ICP (iterative closest point) and its variants for
fine registration of the data to the model. Our algorithm can be used
for matching shapes that overlap only over parts of their extent, for
building models from partial range scans, as well as for simple
symmetry detection, and for matching shapes undergoing articulated
motion.
Bibtex:
@inproceedings{gmgp-rgr-05,
title = "Robust Global Registration",
author = "N.~Gelfand and N.~J.~Mitra and L.~J.~Guibas and H.~Pottmann",
booktitle = "Proc. Symp. Geom. Processing",
pages = "197--206"
year = "2005",
}
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